Space-Time Forecasting Using Soft Geostatistics: A Case Study in Forecasting Municipal Water Demand for Phoenix, AZ

Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions. We use space-time variability in historical data and projections of future population density to improve forecasting of residential water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical moments between a dependent variable, water use, and an independent variable, population density. The independent variable is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating the uncertain estimates into the space-time forecasting process improves forecasting accuracy up to 43.9% over other space-time mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the future, provides up-to-date forecasts of water use, and can be adapted to other socioeconomic and environmental applications.

Per-Pixel Versus Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per- pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Basophil Phenotypes in Chronic Idiopathic Urticaria in Relation to Disease Activity and Autoantibodies

Potentially pathogenic IgG autoantibodies to IgE or its receptor, FcεRIα, have been detected in ~40% of chronic idiopathic urticaria (CIU) patients. CIU patients' basophils display distinct altered FcεRIα-mediated degranulation. CIU patients with basophil histamine release in response to polyclonal goat anti-human IgE ≥10% are classified as CIU responders (CIU-R) and <10% are CIU non-responders (CIU-NR). We compared the presence of autoantibodies to basophil degranulation phenotypes and to disease status (active or inactive). Sera were collected from non-CIU subjects and CIU subjects who participated in a longitudinal study of disease severity and had defined basophil degranulation phenotypes. Immunoenzymetric assays (IEMA) quantified IgG anti-FcεRIα and anti-IgE. IgG anti-FcεRIα antibody was detected in 57% of CIU-R (n=35), 55% of CIU-NR (n=29), and 57% of non-CIU subjects (n=23), whereas IgG anti-IgE was present in 43% of CIU-R, 45% of CIU-NR, and 30% of non-CIU subjects. Both the autoantibody levels and the functional basophil phenotype remained stable in subjects with active disease (n=16), whereas there was an enhancement in basophil function as subjects evolved into a state of remission (n=6), which appears independent of the presence of autoantibody. IEMAs detected a similar frequency of autoantibodies in CIU-R, CIU-NR, and non-CIU subjects. Basophil function may be independent of IEMA-detected autoantibodies

Blue and grey urban water footprints through citizens’ perception and time series analysis of Brazilian dynamics

This is an Accepted Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on 4 March 2021, available online: https://doi.org/10.1080/02626667.2021.1879388Predicting future water demands of societies is a major challenge because it involves a holistic understanding of possible changes within socio-hydrological systems. Although recent research has made efforts to translate social dimensions into the analysis of hydrological systems, few studies have involved citizen participation in water footprint analysis. This paper integrates time series with citizens’ perceptions, knowledge and beliefs concerning sanitation elements to account for municipal blue and grey water footprints in São Carlos, Brazil, from 2009 to 2016, and potential water footprints in 2030 and 2050. In this case study, grey footprint potentially exceeds the blue water footprint by up to 35 times, and volunteered information suggested a reduction in water consumption, larger garbage production and greater investment in sanitation infrastructure from authorities. We conclude that public knowledge can be used to delineate possible water footprint scenarios and reveal paradoxes in the coevolution of socio-hydrological systems on an urban scale

A framework for characterising and evaluating the effectiveness of environmental modelling

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures

Effective modeling for integrated water resource management: a guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management. © 2019 Elsevier Lt

Developing a sustainability science approach for water systems

We convened a workshop to enable scientists who study water systems from both social science and physical science perspectives to develop a shared language. This shared language is necessary to bridge a divide between these disciplines’ different conceptual frameworks. As a result of this workshop, we argue that we should view socio-hydrological systems as structurally co-constituted of social, engineered, and natural elements and study the “characteristic management challenges” that emerge from this structure and reoccur across time, space, and socioeconomic contexts. This approach is in contrast to theories that view these systems as separately conceptualized natural and social domains connected by bi-directional feedbacks, as is prevalent in much of the water systems research arising from the physical sciences. A focus on emergent characteristic management challenges encourages us to go beyond searching for evidence of feedbacks and instead ask questions such as: What types of innovations have successfully been used to address these challenges? What structural components of the system affect its resilience to hydrological events and through what mechanisms? Are there differences between successful and unsuccessful strategies to solve one of the characteristic management challenges? If so, how are these differences affected by institutional structure and ecological and economic contexts? To answer these questions, social processes must now take center stage in the study and practice of water management. We also argue that water systems are an important class of coupled systems with relevance for sustainability science because they are particularly amenable to the kinds of systematic comparisons that allow knowledge to accumulate. Indeed, the characteristic management challenges we identify are few in number and recur over most of human history and in most geographical locations. This recurrence should allow us to accumulate knowledge to answer the above questions by studying the long historical record of institutional innovations to manage water systems

Social science in a water observing system

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95008/1/wrcr12336.pd