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Flood Forecasting Using Machine Learning Methods
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...
Hydrological Modelling and the Water Cycle
This volume is a collection of a selected number of articles based on presentations at the 2005 L’Aquila (Italy) Summer School on the topic of “Hydrologic Modeling and Water Cycle: Coupling of the Atmosphere and Hydrological Models”. The p- mary focus of this volume is on hydrologic modeling and their data requirements, especially precipitation. As the eld of hydrologic modeling is experiencing rapid development and transition to application of distributed models, many challenges including overcoming the requirements of compatible observations of inputs and outputs must be addressed. A number of papers address the recent advances in the State-of-the-art distributed precipitation estima...
Calibration of Watershed Models
Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.
Extreme Natural Hazards, Disaster Risks and Societal Implications
A unique interdisciplinary approach to disaster risk research, including global hazards and case-studies, for researchers, graduate students and professionals.
Water Diplomacy in Action
Complex water problems cannot be resolved by numbers or narratives. Contingent and negotiated approaches are necessary for actionable outcome. In the face of a constantly changing array of interconnected water issues that cross multiple boundaries, the challenge is how to translate solutions that emerge from science and technology into the context of real-world policy and politics. Water Diplomacy in Action addresses this task by synthesizing two emerging ideas––complexity science and negotiation theory––to understand and manage risks and opportunities for an uncertain water future. Rooted in the ideas of complexity science and mutual gains negotiation, this edited volume shows why traditional systems engineering approaches may not work for complex problems, what emerging tools and techniques are needed and how these are used to resolve complex water problems.
Remote Sensing of Drought
Remote Sensing of Drought: Innovative Monitoring Approaches presents emerging remote sensing-based tools and techniques that can be applied to operational drought monitoring and early warning around the world. The first book to focus on remote sensing and drought monitoring, it brings together a wealth of information that has been scattered throughout the literature and across many disciplines. Featuring contributions by leading scientists, it assembles a cross-section of globally applicable techniques that are currently operational or have potential to be operational in the near future. The book explores a range of applications for monitoring four critical components of the hydrological cyc...
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved. The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation.
The Earth Observer
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COVID-19 Pandemic, Geospatial Information, and Community Resilience
"The Open Access version of this book, available at https://www.taylorfrancis.com/books/oa-edit/10.1201/9781003181590, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license." Geospatial information plays an important role in managing location dependent pandemic situations across different communities and domains. Geospatial information and technologies are particularly critical to strengthening urban and rural resilience, where economic, agricultural, and various social sectors all intersect. Examining the United Nations' SDGs from a geospatial lens will ensure that the challenges are addressed for all populations in different locations. This book, with worldwide contributions focused on COVID-19 pandemic, provides interdisciplinary analysis and multi-sectoral expertise on the use of geospatial information and location intelligence to support community resilience and authorities to manage pandemics.
