2,290 research outputs found

    Flood dynamics derived from video remote sensing

    Get PDF
    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Unleashing the power of artificial intelligence for climate action in industrial markets

    Get PDF
    Artificial Intelligence (AI) is a game-changing capability in industrial markets that can accelerate humanity's race against climate change. Positioned in a resource-hungry and pollution-intensive industry, this study explores AI-powered climate service innovation capabilities and their overall effects. The study develops and validates an AI model, identifying three primary dimensions and nine subdimensions. Based on a dataset in the fast fashion industry, the findings show that the AI-powered climate service innovation capabilities significantly influence both environmental and market performance, in which environmental performance acts as a partial mediator. Specifically, the results identify the key elements of an AI-informed framework for climate action and show how this can be used to develop a range of mitigation, adaptation and resilience initiatives in response to climate change

    Influência do uso e ocupação do solo na evapotranspiração utilizando técnicas de sensoriamento remoto para a bacia do Xingu

    Get PDF
    Changes in the use and land cover of a watershed have significant impacts on hydrological processes and water balance variables, such as real evapotranspiration (ETr), a component of the hydrological cycle evaluated as one of the most affected by changes in the type of watershed cover. surface. Allied to the fact, the technique of remote sensing has become an excellent tool for assessing environmental degradation, as it allows analyzing the changes caused by anthropic action in temporal and spatial scales in complex environments of hydrographic basins. In this context, focused on the Xingu Hydrographic Basin, and its five sub-basins (Lower Xingu, Middle Xingu, Upper Xingu, Iriri and Nascentes do Xingu), the present work aimed to: evaluate the performance of seven ETr products (FLDAS , MOD16A2, PML_V2, TerraClimate, ERA5-Land, GLEAM_v3.3a and SSEBop) and the upscaling of FLUXCOM, compared to the median of the eight models in the common period from 2003 to 2014; to study the influence of changes in land use and land cover on the ETr, estimated by the product created through the median of the eight models, relating it to the data from MapBiomas, in the available interval of 1985-2020; and to analyze the effects on real evapotranspiration arising before (1993-2015) and after (2016-2020) the filling of the reservoir of the Belo Monte Hydroelectric Power Plant, through the SSEBop BR Evapotranspiration application, which uses images from the Landsat 5, 7 and 8 series TOA reflectance in obtaining the ETr. All datasets described were accessed and processed through the Google Earth Engine platform. For most analyses, the results found suggested that the products MOD16A2 and GLEAM_v3.3a returned data closer to the median of the models, with convergence of evapotranspiration values around 93.5% and 91.7%, respectively; decrease in forest areas (-16.23%), with conversion to pasture areas, in the order of +12.51%, and agricultural areas, reaching +5.5%, with the maximum peak of ET during the season conversion (October-November); and two trends in the average ETr related to the construction of the Belo Monte HPP, with a downward trend of 0.066 mm/d in the period prior to the reservoir (1993-2015), and an upward trend after its filling (2016-2020) equal to 0.040 mm /d.As modificações no uso e cobertura do solo de uma bacia têm impactos significativos nos processos hidrológicos e nas variáveis do balanço hídrico, tais como a evapotranspiração real (ETr), componente do ciclo hidrológico avaliada como uma das mais afetadas pela alteração do tipo de cobertura da superfície. Aliado ao fato, a técnica do sensoriamento remoto vem se tornando uma excelente ferramenta para avaliação da degradação ambiental, pois permite analisar as alterações provocadas pela ação antrópica nas escalas temporal e espacial em ambientes complexos de bacias hidrográficas. Nesse contexto, focado na Bacia Hidrográfica do Xingu, e suas cinco sub-bacias (Baixo Xingu, Médio Xingu, Alto Xingu, Iriri e Nascentes do Xingu), o presente trabalho teve como objetivo: avaliar o desempenho de sete produtos de ETr (FLDAS, MOD16A2, PML_V2, TerraClimate, ERA5-Land, GLEAM_v3.3a e SSEBop) e do upscalling do FLUXCOM, frente à mediana dos oito modelos no período comum de 2003 a 2014; estudar a influência das mudanças no uso e cobertura do solo sobre a ETr, estimada pelo produto criado através da mediana dos oito modelos, relacionando-a aos dados do MapBiomas, no intervalo disponível de 1985-2020; e analisar os efeitos na evapotranspiração real oriundos antes (1993-2015) e após (2016-2020) o enchimento do reservatório da Hidrelétrica de Belo Monte, por meio do aplicativo SSEBop BR Evapotranspiration, que utiliza imagens da série Landsat 5, 7 e 8 TOA reflectance na obtenção da ETr. Todos os conjuntos de dados descritos foram acessados e processados por meio da plataforma Google Earth Engine. Para a maioria das análises, os resultados encontrados sugeriram que os produtos MOD16A2 e GLEAM_v3.3a retornaram dados mais próximos à mediana dos modelos, com convergência de valores de evapotranspiração em torno de 93,5% e 91,7%, respectivamente; decréscimo nas áreas de floresta (-16,23%), com conversão às áreas de pastagens, na ordem de +12,51%, e áreas agrícolas, chegando a +5,5%, sendo o pico máximo da ET durante a estação de conversão (outubro-novembro); e duas tendências na ETr média relacionada à construção da UHE Belo Monte, sendo tendência de decréscimo de 0,066 mm/d no período anterior ao reservatório (1993-2015), e tendência de acréscimo após seu enchimento (2016-2020) igual a 0,040 mm/d

    Systems Analysis for Sustainable Wellbeing. 50 years of IIASA research, 40 years after the Brundtland Commission, contributing to the post-2030 Global Agenda

    Get PDF
    This report chronicles the half-century-long history of the International Institute for Applied Systems Analysis (IIASA), established in 1972 in Laxenburg, Austria, to address common social, economic, and environmental challenges at a time when the world was politically dominated by the Cold War. The report shows IIASA’s transition from its original raison d’être as a cooperative scientific venture between East and West to its position today as a global institute engaged in exploring solutions to some of the world’s most intractable problems—the interconnected problems of population, climate change, biodiversity loss, land, energy, and water use, among others. It provides a concise overview of IIASA’s key contributions to science over the last 50 years and of the advances it has made not only in analyzing existing and emerging trends but also in developing enhanced scientific tools to address them. The report also shows how IIASA is currently working with distinguished partners worldwide to establish the scientific basis for a successful transition to sustainable development. The global mandate, to achieve the 2030 Agenda, its 17 Sustainable Development Goals (SDGs), and 169 specific targets, features prominently in the institute’s work and in the report at hand: the pathways needed to achieve the SDGs have been the basis of many scientific studies by IIASA and its partners. The predominantly “bottom-up” nature of tackling the SDGs has required optimal responses to the very diverse and overlapping issues they involve, including judicious tradeoffs among the solutions that can be applied. Now, at the mid-term review point of the 2030 Agenda, this report focuses on the big picture and clarifies why, after years of scientific endeavor, the ultimate goal of this difficult global mandate should be sustainable wellbeing for all. The report is in six parts that summarize past and current IIASA research highlights and point toward future challenges and solutions: i) Systems analysis for a challenged world; ii) Population and human capital; iii) Food security, ecosystems, and biodiversity; iv) Energy, technology, and climate change; v) Global systems analysis for understanding the drivers of sustainable wellbeing; and vi) Moving into the future: Three critical policy messages. The three critical policy messages, necessary to trigger discussions about a post-2030 Agenda for Sustainable Development are: (1) Suboptimization is suboptimal: Mainstream a systems-analysis approach into policymaking at all levels. (2) Enhance individual agency: Prioritize women’s empowerment through universal female education; and (3) Strengthen collective action and governance: Global cooperation and representation for the global common

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Towards nowcasting in Europe in 2030

    Get PDF
    The increasing impact of severe weather over Europe on lives and weathersensitive economies can be mitigated by accurate 0–6 h forecasts (nowcasts), supporting a vital ‘last line of defence’ for civil protection and many other applications. Recognizing lack of skill in some complex situations, often at convective and local sub-kilometre scales and associated with rare events, we identify seven recommendations with the aim to improve nowcasting in Europe by the national meteorological and hydrological services (NMHSs) by 2030. These recommendations are based on a review of user needs, the state of the observing system, techniques based on observations and high-resolution numerical weather models, as well as tools, data and infrastructure supporting the nowcasting community in Europe. Denser and more accurate observations are necessary particularly in the boundary layer to better characterize the ingredients of severe storms. A key driver for improvement is next-generation European satellite data becoming available as of 2023. Seamless ensemble prediction methods to produce enhanced weather forecasts with 0–24 h lead times and probabilistic products require further development. Such products need to be understood and interpreted by skilled forecasters operating in an evolving forecasting context
    corecore