Climate change and land use transformations have induced an increased flood risk worldwide. These phenomena are dramatically impacting ordinary life and the economy. Research and technology offer a new strategy to quantify and predict such phenomena and also mitigate the impact of flooding. In particular, the growing computational power is offering new strategies for a more detailed description of the flooding over large scales. This book offers an overview of the most recent outcomes of the research on this argument.

Climate change and land use transformations have induced an increased flood risk worldwide. These phenomena are dramatically impacting ordinary life and the economy. Research and technology offer a new strategy to quantify and predict such phenomena and also mitigate the impact of flooding. In particular, the growing computational power is offering new strategies for a more detailed description of the flooding over large scales. This book offers an overview of the most recent outcomes of the research on this argument.

Global Flood Hazard Subject Category Winner, PROSE Awards 2019, Earth Science Selected from more than 500 entries, demonstrating exceptional scholarship and making a significant contribution to the field of study. Flooding is a costly natural disaster in terms of damage to land, property and infrastructure. This volume describes the latest tools and technologies for modeling, mapping, and predicting large-scale flood risk. It also presents readers with a range of remote sensing data sets successfully used for predicting and mapping floods at different scales. These resources can enable policymakers, public planners, and developers to plan for, and respond to, flooding with greater accuracy and effectiveness. Describes the latest large-scale modeling approaches, including hydrological models, 2-D flood inundation models, and global flood forecasting models Showcases new tools and technologies such as Aqueduct, a new web-based tool used for global assessment and projection of future flood risk under climate change scenarios Features case studies describing best-practice uses of modeling techniques, tools, and technologies Global Flood Hazard is an indispensable resource for researchers, consultants, practitioners, and policy makers dealing with flood risk, flood disaster response, flood management, and flood mitigation.

Recent advances in the modeling and remote sensing of droughts and floods Droughts and floods are causing increasing damage worldwide, often with devastating short- and long-term impacts on human society. Forecasting when they will occur, monitoring them as they develop, and learning from the past to improve disaster management is vital. Global Drought and Flood: Observation, Modeling, and Prediction presents recent advances in the modeling and remote sensing of droughts and floods. It also describes the techniques and products currently available and how they are being used in practice. Volume highlights include: Remote sensing approaches for mapping droughts and floods Physical and statistical models for monitoring and forecasting hydrologic hazards Features of various drought and flood systems and products Use by governments, humanitarian, and development stakeholders in recent disaster cases Improving the collaboration between hazard information provision and end users The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Flooding has been the world most frequent natural disaster over the past decades. Near realtime flood mapping is critical for mitigating the devastating impacts of flooding (e.g., life/economic losses). The United Nations (UN) Sustainable Development Goal 11 (2015-2030) is calling for global efforts to monitor large-scale floods in near realtime for protecting the vulnerable communities. Remote sensing (RS) has the unique capability of earth observation with applications in global disaster response. Recent advances in micro-satellite constellations are offering opportunities for daily satellite image acquisition with a high spatial resolution. Such a high temporal resolution (i.e., daily) is the key to the success of RS for near realtime global flood monitoring. Moreover, the large volume of RS imagery further unleashes the vast potential of data-driven machine learning (ML) approaches for accurate flood mapping. Recent developments in deep learning, a sub-field of ML, have demonstrated the superiority of ML in flood mapping given massive RS data and corresponding labels. Unfortunately, training labels are very scarce in the domain of disaster response. This dissertation explores ML models for flood mapping without massive training labels by introducing the prior knowledge in RS data including the spatial, temporal, and spectral information: First, this dissertation proposes the PSNet to model flood mapping as bitemporal change detection by leveraging the temporal information of RS data. With only a few hand labels, the model can map floods with an over 0.85 F1 score for various heterogeneous flooded areas. Second, to further accelerate flood mapping, a self-supervised bitemporal change detection model, Patch-CVA, is developed to detect changed areas without hand labels. Further post-processing removes most false alarms by introducing the spatial and spectral information of floodwaters. Third, while the Patch-CVA has achieved good performance, its underlying assumption (i.e., most changes are flooded areas) may not hold in certain environments (e.g., crop growth). This dissertation proposes a weak label generation framework by fusing the spatial, temporal, and spectral information of geospatial data. A large weakly labeled dataset is obtained to train data-driven ML models for pixel-wise flood mapping with an over 0.9 F1 score.

Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.

Earth Observation for Flood Applications: Progress and Perspectives describes the latest scientific advances in Earth Observation. With recent floods around the world becoming ever more devastating, there is a need for better science enabling more effective solutions at a fast pace. This book aims at stretching from the current flood mapping to diverse real data so as to estimate the flood risk and damage. Earth Observation for Flood Applications: Progress and Perspectives includes three parts containing each a separate but complementary topic area under floods. Each chapter unfolds various applications, case studies, and illustrative graphics. In terms of flood mapping and monitoring, the usage of multi-sensor satellite data, web-services information, microwave remote sensing methods are discussed in depth. So, this book is a valuable resource for scientists, researchers, and students in the area of earth observation. - Focuses in on one specific application field of Earth Observation - Brings the latest scientific advances and perspectives from experts around the world - Includes extensive figures, tables, and case studies to illustrate real-life applications