Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancem...Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.展开更多
As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and syste...As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and systems are an urgent need. Vulnerability has been widely discussed and several flood projection tools have been developed using complex hydrological models. However, despite the significant contribution of flood projection maps to predicting the impact of potential floods, they are difficult and impractical to use by stakeholders and policy makers, while they have proven to be inefficient and out of date in several cases. This research aims to cover the gaps in coastal and riverine flood management, developing a method that models flood patterns, using geospatial data of past large flood disasters. The outcomes of this research produce a five scale vulnerability assessment method, which could be widely implemented in all sectors, including transport, critical infrastructure, public health, tourism, constructions etc. Moreover, they could facilitate decision making and provide a wide range of implementation by all stakeholders, insurance agents, land-use planners, risk experts and of course individual. According to this research, the majority of the elements exposed to flood hazards, lay at specific combinations between 1) elevation (Ei) and 2) distance from water-masses (Di), expressed as (Ei, Di), including: 1) in general landscapes: ([0 m, 1 m), [0 km, 6 km), [0 m - 3 m), [0 km, 3 km)) and ([0 m - 6 m), [0 km, 1 km)), 2) in low laying regions: ([0 m, 1 m), [0 km, 40 km), [0 m - 3 m), [0 km, 30 km)) and ([0 m - 6 m), [0 km, 15 km)) and 2) in riverine regions: ([0 m, 4 m), [0 km, 3 km)). All elements laying on these elevations and distances from water masses are considered extremely and highly vulnerable to flood extremes.展开更多
文摘Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.
文摘As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and systems are an urgent need. Vulnerability has been widely discussed and several flood projection tools have been developed using complex hydrological models. However, despite the significant contribution of flood projection maps to predicting the impact of potential floods, they are difficult and impractical to use by stakeholders and policy makers, while they have proven to be inefficient and out of date in several cases. This research aims to cover the gaps in coastal and riverine flood management, developing a method that models flood patterns, using geospatial data of past large flood disasters. The outcomes of this research produce a five scale vulnerability assessment method, which could be widely implemented in all sectors, including transport, critical infrastructure, public health, tourism, constructions etc. Moreover, they could facilitate decision making and provide a wide range of implementation by all stakeholders, insurance agents, land-use planners, risk experts and of course individual. According to this research, the majority of the elements exposed to flood hazards, lay at specific combinations between 1) elevation (Ei) and 2) distance from water-masses (Di), expressed as (Ei, Di), including: 1) in general landscapes: ([0 m, 1 m), [0 km, 6 km), [0 m - 3 m), [0 km, 3 km)) and ([0 m - 6 m), [0 km, 1 km)), 2) in low laying regions: ([0 m, 1 m), [0 km, 40 km), [0 m - 3 m), [0 km, 30 km)) and ([0 m - 6 m), [0 km, 15 km)) and 2) in riverine regions: ([0 m, 4 m), [0 km, 3 km)). All elements laying on these elevations and distances from water masses are considered extremely and highly vulnerable to flood extremes.
