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Validating A Flood Vulnerability Model Using High Spatial Resolution Cubesat Imagery And Socio Economic Data In A Us Coastal Region
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Book Synopsis Validating a Flood Vulnerability Model Using High Spatial Resolution Cubesat Imagery and Socio-economic Data in a U.S. Coastal Region by : Christoper Hidalgo
Download or read book Validating a Flood Vulnerability Model Using High Spatial Resolution Cubesat Imagery and Socio-economic Data in a U.S. Coastal Region written by Christoper Hidalgo and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Based Flood Risk Modeling Using Features from Satellite, Socioeconomic, Geographic, and Building Data by : Anushka Ray
Download or read book Machine Learning Based Flood Risk Modeling Using Features from Satellite, Socioeconomic, Geographic, and Building Data written by Anushka Ray and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the effects of climate change coupled with increased urbanization, many cities will be experiencing more frequent and intense flooding in the future. As a result, it would be very beneficial for urban planners to have a low-cost and efficient modeling tool that can determine the flood risk at a granular level such as the census tract. Boston is one such coastal urban city that will experience an increase in flooding. Since each census tract in Boston is unique and varies in population and land use, urban planners and policy makers must know which areas in Boston are the most vulnerable to provide them with resources. This research proposes a machine learning based model that evaluates the flood risk of census tracts in Boston. The overall flood risk of a census tract is determined by aggregating relevant features such as land cover data from aerial satellite imagery via semantic segmentation methods, elevation, slope, and flow accumulation. In addition to these flood hazard features, we also integrate flood vulnerability features from socioeconomic data and building information for each census tract.
Book Synopsis Social and Environmental Vulnerability to Flooding by : Selena Hinojos
Download or read book Social and Environmental Vulnerability to Flooding written by Selena Hinojos and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flooding is a natural hazard that touches nearly all facets of the globe in some capacity. The exposure of communities to flooding is only projected to become more frequent and intensified due to climate and land-use change; therefore, it is vital to understand how flooding impacts are distributed across populations. An approach to mapping the landscape of geographic flood-disadvantaged communities is through the use of a social vulnerability index. This measurement scheme is reliant on aggregated socioeconomic and demographic data that can be curated at several scales and subject to the effects of the Modifiable Areal Unit Problem (MAUP). Understanding how the selection of scale influences flood vulnerability results is relatively limited in literature and advantageous as this information could inform future decision-making for allocating resources that support communities most in need. This multi-scale flood risk analysis integrated social vulnerability, land cover, and flood hazard data to investigate the relationship of vulnerable populations to varying levels of flood exposure across the block group, tract, and county scales within coastal Virginia -- a highly populated region facing land subsidence, unusually high tides, rising sea level, and elevated surface and riverine flooding. The deviation and similarities of social correlates of vulnerability to flooding were investigated across scales. Additionally, the geography of local clusters and spatial outliers of social vulnerability to flood exposure, determined through a bivariate Local Indicators of Spatial Association (LISA) analysis, was utilized to identify social inequities within the floodplain and how those deviated across scales. I found that the aggregation of geographic units and the selection of scale has considerable impacts on the social vulnerability and flood risk results. There are instances where increased aggregated scales significantly undercounted highly vulnerable populations. Similar trends occurred for areas of high vulnerability and varying exposure which are target locations for current and future flood risk reduction. I also found that generally based on the integrated scale, the landscape of vulnerability and flood risk can identify different priority areas which can be a real-life consequence of the MAUP. These results warrant the discussion of understanding the implications of scale selection on research methodological approaches and what this means for practitioners and policymakers that utilize social vulnerability information to help guide flood mitigation strategies.
Book Synopsis New Multiple-scale Technique for the Assessment of Relative Flood Vulnerability by : Mohamed Mahgoub
Download or read book New Multiple-scale Technique for the Assessment of Relative Flood Vulnerability written by Mohamed Mahgoub and published by . This book was released on 2014 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study new, a flood vulnerability (FLV) indices using socio-economic and loss-estimation parameters from Hazus-MH were created to relatively assess flood vulnerable among Illinois's jurisdictions. Hazus-MH is nationally standardized software program developed by Federal Emergency Management Agency (FEMA) that estimates the potential economic losses and limited social impacts for a select suite of natural hazards. Here we defined FLV as combined function of potential building-related-economic losses and socioeconomic factors. The Hazus-MH loss estimation software and FEMA's 100-year regulatory floodplain were used to estimate flood building-related-economic losses. To assess the relative differences in the potential socioeconomic consequences of flooding among Illinois jurisdictions, social vulnerability (SoV) indices were calculated using six socioeconomic indicators available within Hazus-MH's demographic database. Total estimated the building-related-flood exposure within the 100-year floodplain in Illinois was estimated to be [approximately]$190.25 billion (2006 dollars). The city of Chicago and the adjoining counties were estimated to have the greatest flood exposure. These counties accounted for approximately 65% of the total flood exposure located within Illinois's 100-year floodplains. The estimation of total building-related-flood loss within the 100-year floodplain was $18.03 billion (2006 dollars). The FLV assessment results indicated that urban and suburban communities are relatively more resilient to flooding. This is likely due to the availability of preventative resources such as well-constructed buildings to resist flood damages, existence of good levee systems, land use regulation for flood-prone site, and evacuation programs. While more rural, river communities in southern Illinois tend to be more vulnerable to flood because they lack the "preventative resource" found in Illinois's urban and suburban communities. Exceptions to the more flood resilient urban and suburban communities were found in poor urban jurisdictions located in southern Cook County. These communities tended have some of the highest SoV scores in the state. However, these communities generally had only minor to moderate to flood exposure suggesting there elevated FLV was largely driven by their SoV scores. In addition to southern Cook County in northeastern Illinois, SoV analysis results indicate that there were clustering of relatively high-social vulnerability in counties adjacent to Cook, Du Page, and Will. Jurisdictions in Southern Illinois with elevated FLV were located in Alexander, Pulaski, Williamson, and Saline Counties. Properly understanding and relatively assessing flood vulnerability can lead to a more complete and informative flood hazard assessment. This cannot be accomplished without taking into consideration the two main elements of flood vulnerability, economic losses and SoV. The SoV indices provide an experimental foundation for comparing spatial disparity within a SoV context across a large and diverse geographic area such as the State of Illinois. Flood and social vulnerability indices can be a useful approach in flood-risk planning and mitigation efforts. Moreover, the SoV score could be used as a measurement tool to prioritize high socially vulnerable areas which are more in need of assistance, while those areas with low social vulnerability scores might be expected to be more self-sufficient at mitigating their flood risk or recovering from a flood disaster.
Book Synopsis Spatial Vulnerability Analysis and Thematic Mapping of Urban Floods by : Mohammad Khalid Hossain
Download or read book Spatial Vulnerability Analysis and Thematic Mapping of Urban Floods written by Mohammad Khalid Hossain and published by . This book was released on 2020 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The social and economic impact of urban flooding is becoming more severe in the United States over time. Urban areas are mostly vulnerable to flash floods because of the impervious surface, which increases the surface runoff. More than 80 percent of people live in urban areas in the United States, and they are at higher risk of urban flooding. Although many urban areas have a higher risk of urban flooding, there is still a significant knowledge gap of understanding between the minority's and nonminority's vulnerability to urban floods. Therefore, using Birmingham, Alabama, as a study area, this research designs a quantitative thematic mapping method to assess the flood risks of urban population and buildings. In this research, census data was used to assess urban residents' vulnerability to flooding using thematic mapping method – location quotient (LQ) and compare it with the widely used social vulnerability index. The findings suggest that the aggregation of White populations is much higher compared to minorities. This research also developed a flood risk model using integrated GIS and cartographic approach considering different environmental factors that influence the urban floods. This study found that the Valley Creek area is the highest flood risk zone in Birmingham, and has the highest percentage of residential (i.e., 56.14 %) and commercial (i.e., 75.34 %) buildings located in very high flood risk areas. The decennial census data from 1990 to 2015 was used to examine whether vulnerable population groups aggregated more in the flooding areas or moved away from Birmingham's flooding areas in the past thirty years. The findings of this research indicate that most minorities are aggregating more in the floodplain areas, whereas the non-minorities are moving away from the flooding regions.
Book Synopsis Spatial Modelling of Flood Risk and Flood Hazards by : Biswajeet Pradhan
Download or read book Spatial Modelling of Flood Risk and Flood Hazards written by Biswajeet Pradhan and published by Springer Nature. This book was released on 2022-04-06 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Floods and flash floods with hydro-meteorological and tropical cyclones are the some of the most devastating natural disasters causing massive damages to natural and man-made features. Flood hazards are a major threat to human life, properties (agricultural area, yield production, building and homes) and infrastructures (bridges, roads, railways, urban infrastructures, etc). Flood hazards susceptibility mapping (risk assessment) and modelling is an essential step for early warning systems, emergency services, prevention and mitigation of future environmental and social hazards and implementation of risk management strategies. Due to the lack of proper information, technology-based policies and strategies, mapping and modelling can often not be implemented to the best possible level. Geo-spatial techniques have enjoyed rising interest in recent decades among the earth environmental and social sciences research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward sustainable earth and human society. By linking geo-spatial computational intelligence techniques with societal and environmental-oriented problems, this book demonstrates geospatial technology approaches to data mining techniques, data analysis, modelling, risk assessment and visualization and management strategies in different aspects of flood hazards. We believe that a diverse group of academics, scientists, geographers, hydrologist, remote sensing and GIS expertise, environmentalists, meteorologists and computing experts with a common interest in geospatial sciences within the earth environmental sciences and humanistic and social sciences will find this book to be of great value.
Book Synopsis Coastal Flooding Storm Surge Model: User's guide by :
Download or read book Coastal Flooding Storm Surge Model: User's guide written by and published by . This book was released on 1981 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309492432 Total Pages :29 pages Book Rating :4.3/5 (94 download)
Book Synopsis Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space by : National Academies of Sciences, Engineering, and Medicine
Download or read book Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-06-18 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018) provides detailed guidance on how relevant federal agencies can ensure that the United States receives the maximum benefit from its investments in Earth observations from space, while operating within realistic cost constraints. This short booklet, designed to be accessible to the general public, provides a summary of the key ideas and recommendations from the full decadal survey report.
Book Synopsis Machine Learning and Remote Sensing for Near Realtime Flood Mapping by : Bo Peng
Download or read book Machine Learning and Remote Sensing for Near Realtime Flood Mapping written by Bo Peng and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Characterizing Compound Coastal Flood Risk in Urbanized Communities by : Joseph Lucey
Download or read book Characterizing Compound Coastal Flood Risk in Urbanized Communities written by Joseph Lucey and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Coastal flooding is a growing concern. Compound coastal flooding considers the joint impacts of marine and hydrologic events characterized by multiple flooding pathways (i.e., high offshore water levels, streamflow, energetic waves, precipitation) acting concurrently. Flood risks are commonly assessed using numerical models or statistical methods. Quantifying event uncertainty is critical to accurate flood risk assessment. This work develops a hybrid statistical-hydrodynamic flood modeling methodology to characterize flood mapping uncertainty in highly urbanized, tidally and wave dominated regions. Uncertainties associated with copula selection, sampling method, data record length, utilized rainfall gauge, and event choice along an isoline were considered. Univariate statistics are analyzed for individual sites and events. Conditional and joint probabilities are developed using a range of copulas, sampling methods, and hazard scenarios. Multiple copulas (Nelsen, BB1, BB5, and Roch-Alegre, Fischer-Koch) consistently passed a Cram\'er-von Mises test and presented similar event pairs, with the exception of the BB5 copula which was often more conservative (i.e., more severe event pairs). Sampling impacts are considered using annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Generally, annual maximum sampling yielded the largest (most severe) event pairs. However, in some cases wet season monthly coinciding sampling suggested higher marine water levels. Uncertainties associated with record length were quantified by creating subsets with different sizes from long data records (~100 years). Significant event pair variability was observed when using short data record lengths, although results stabilized at 70-years. Flood risk estimates using local rainfall gauges significantly varied suggesting microclimatologies must be considered in flood risk analysis. Validated Delft3D-FM hydrodynamic models were developed for multiple urbanized coastal communities. Compound events were simulated to quantify flood mapping uncertainties associated with statistical characterization.
Book Synopsis Development of a Multi-Scale Observation Product to Aid Numerical Model Simulations During Flood Emergencies by : Elena Sava
Download or read book Development of a Multi-Scale Observation Product to Aid Numerical Model Simulations During Flood Emergencies written by Elena Sava and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation introduces a new Multi-Scale Observation Product (MOP) for flood assessment. The MOP integrates traditional observations with new, low cost, opportunistic data to create inundation maps at a high spatial and temporal resolution. A fusion technique is developed that combines satellite remote sensing imagery with aerial photography and user-generated data shared through social network outlets and provides a characterization of uncertainty. The goal is to augment remote sensing imagery, which is characterized by high spatial but low temporal resolution, with near real-time data that are often spatially sparse, but stream at a high temporal velocity throughout a flood emergency.The MOP is tested on three separate cases of flood events that occurred throughout the U.S. Results show that MOP can be generated using a variety of different data sources, and flood inundations estimated are comparable with a simulated output from numerical models, Hydrologic Engineering Center River Analysis System (HEC-RAS), Flood2D-GPU, and WRF-Hydro. Likewise, results showed that inundation and confidence levels derived from the MOP correspond with observation data from the Federal Emergency Management Agency (FEMA) products. Lastly, water level heights extracted from photographs shared through social media showed encouraging results as a strong agreement was seen between model output and field measurements.This research was driven by the need for high spatial and temporal resolution flood products for flood modeling. More specifically, hydrological models are now run at very fine resolutions (hyper-resolution modeling), but there is a lack of observation-based products at these high resolutions. The MOP product could be used to assess inundation extents, calibrate model simulations, initialize conditions, and identification of optimal model parameters.
Book Synopsis Reducing Coastal Risk on the East and Gulf Coasts by :
Download or read book Reducing Coastal Risk on the East and Gulf Coasts written by and published by . This book was released on 2014 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Hurricane- and coastal-storm-related losses have increased substantially during the past century, largely due to increases in population and development in the most susceptible coastal areas. Climate change poses additional threats to coastal communities from sea level rise and possible increases in strength of the largest hurricanes. Several large cities in the United States have extensive assets at risk to coastal storms, along with countless smaller cities and developed areas. The devastation from Superstorm Sandy has heightened the nation's awareness of these vulnerabilities. What can we do to better prepare for and respond to the increasing risks of loss? Reducing Coastal Risk on the East and Gulf Coasts reviews the coastal risk-reduction strategies and levels of protection that have been used along the United States East and Gulf Coasts to reduce the impacts of coastal flooding associated with storm surges. This report evaluates their effectiveness in terms of economic return, protection of life safety, and minimization of environmental effects. According to this report, the vast majority of the funding for coastal risk-related issues is provided only after a disaster occurs. This report calls for the development of a national vision for coastal risk management that includes a long-term view, regional solutions, and recognition of the full array of economic, social, environmental, and life-safety benefits that come from risk reduction efforts. To support this vision, Reducing Coastal Risk states that a national coastal risk assessment is needed to identify those areas with the greatest risks that are high priorities for risk reduction efforts. The report discusses the implications of expanding the extent and levels of coastal storm surge protection in terms of operation and maintenance costs and the availability of resources. Reducing Coastal Risk recommends that benefit-cost analysis, constrained by acceptable risk criteria and other important environmental and social factors, be used as a framework for evaluating national investments in coastal risk reduction. The recommendations of this report will assist engineers, planners and policy makers at national, regional, state, and local levels to move from a nation that is primarily reactive to coastal disasters to one that invests wisely in coastal risk reduction and builds resilience among coastal communities."--Publisher's description.
Book Synopsis Comparing Hydrodynamic Models and GIS for Coastal Flood Vulnerability Assessment by : Avidesh Seenath
Download or read book Comparing Hydrodynamic Models and GIS for Coastal Flood Vulnerability Assessment written by Avidesh Seenath and published by . This book was released on 2015 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Geospatial Analysis of Potential Flooding from Storm Surge and Sea-level Change on the Texas Coast by 2100 by : Gennadii Prykhodko
Download or read book Geospatial Analysis of Potential Flooding from Storm Surge and Sea-level Change on the Texas Coast by 2100 written by Gennadii Prykhodko and published by . This book was released on 2020 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tropical cyclones pose a major hazard to the State of Texas. Anthropogenic climate change and global warming have the potential to increase extreme weather events and lead to enhanced flood hazard zones. This study aims to analyze potential flooding from hurricane induced storm tides in the Houston metroplex area, and particularly in Harris County. A geospatial framework is proposed for modeling inundation from storm surge with sea-level rise. Spatial models and statistics are applied to the State of Texas with the focus on the Houston metropolitan area including Harris County. Historical and observational tropical cyclone data is used to examine past storm events and to construct spatial distribution models. Climatology of tropical cyclones reveals increasing rates of hurricanes Category 3 and higher in the Atlantic basin. Spatial modeling of local tropical cyclone frequency and intensity in combination with population exposure demonstrates that Harris County is at high risk for hurricane hazards. SLOSH model simulations of Category 5 hurricane storm surges show that under present day sea-level, a significant portion of Harris County is at risk of storm surge flooding. When combined with sea-level rise, magnitude and extent of flooding are amplified. Exposure of population, highway infrastructure and critical structures and facilities is investigated. Modeling of hurricane storm tide with projected sea-level rise scenarios shows that Harris County is extremely vulnerable to flooding under changing climate. The results suggest that a greater amount of this region's infrastructure is projected to be exposed to damage from hurricane storm surge under sea-level rise scenarios. An expected increase in the frequency of intense hurricanes and sea-levels further exacerbates the vulnerability of this region. Under RCP 8.5, anthropogenic induced climate change is likely to substantially increase the vulnerability of people and infrastructure in greater Houston area to storm surge flooding by 2100.
Book Synopsis Social Vulnerability, Green Infrastructure, Urbanization and Climate Change-induced Flooding by : Chingwen Cheng
Download or read book Social Vulnerability, Green Infrastructure, Urbanization and Climate Change-induced Flooding written by Chingwen Cheng and published by . This book was released on 2013 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change is projected to increase the intensity and frequency of storm events that would increase flooding hazards. Urbanization associated with land use and land cover change has altered hydrological cycles by increasing stormwater runoff, reducing baseflow and increasing flooding hazards. Combined urbanization and climate change impacts on long-term riparian flooding during future growth are likely to affect more socially vulnerable populations. Growth strategies and green infrastructure are critical planning interventions for minimizing urbanization impacts and mitigating flooding hazards. Within the social-ecological systems planning framework, this empirical research evaluated the effects of planning interventions (infill development and stormwater detention) through a risk assessment in three studies. First, a climate sensitivity study using SWAT modeling was conducted for building a long-term flooding hazard index (HI) and determining climate change impact scenarios. A Social Vulnerability Index (SoVI) was constructed using socio-economic variables and statistical methods. Subsequently, the long-term climate change-induced flooding risk index (RI) was formulated by multiplying HI and SoVI. Second, growth strategies in four future growth scenarios developed through the BMA ULTRA-ex project were evaluated through land use change input in SWAT modeling and under climate change impact scenarios for the effects on the risk indices. Third, detention under climate sensitivity study using SWAT modeling was investigated in relation to long-term flooding hazard indices. The results illustrated that increasing temperature decreases HI while increasing precipitation change and land use change would increase HI. In addition, there is a relationship between climate change and growth scenarios which illustrates a potential threshold when the impacts from land use and land cover change diminished under the High impact climate change scenario. Moreover, spatial analysis revealed no correlation between HI and SoVI in their current conditions. Nevertheless, the Current Trends scenario has planned to allocate more people living in the long-term climate change-induced flooding risk hotspots. Finally, the results of using 3% of the watershed area currently available for detention in the model revealed that a projected range of 0 to 8% watershed area would be required to mitigate climate change-induced flooding hazards to the current climate conditions. This research has demonstrated the value of using empirical study on a local scale in order to understand the place-based and watershed-specific flooding risks under linked social-ecological dynamics. The outcomes of evaluating planning interventions are critical to inform policy-makers and practitioners for setting climate change parameters in seeking innovations in planning policy and practices through a transdisciplinary participatory planning process. Subsequently, communities are able to set priorities for allocating resources in order to enhance people's livelihoods and invest in green infrastructure for building communities toward resilience and sustainability.
Book Synopsis Quantifying River-floodplain Connectivity of the Lower Rio Grande by : Sara Esther Davila
Download or read book Quantifying River-floodplain Connectivity of the Lower Rio Grande written by Sara Esther Davila and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydrological connectivity is the movement of energy and matter between channels to an adjacent floodplain. When there is a hydrological connection between river and floodplain, it is classified as river-floodplain connectivity. Characterizing river-floodplain connectivity can provide useful information for land and flood management, as well as ecological enhancement. The Lower Rio Grande River is controlled by anthropogenic features designed for energy generation, agricultural production, and water diversion for regions like the Rio Grande Valley (RGV), Texas, USA, and the state of Tamaulipas in Mexico. An increase in flood events along the RGV would cause concern over the riverine interactions with the adjacent floodplains during periods of high discharges and/or rainfall. Numerical models can help in this regard by characterizing river-floodplain systems but are often limited by the scarcity of high-resolution topographic data for flat, low-sloped regions like the Lower Rio Grande area. This limitation of data has hindered the development of accurate numerical models of the Lower Rio Grande region as the dynamic nature of extreme weather requires periodic model updates with the most recent topographic, hydrologic, and hydraulic conditions of the Lower Rio Grande region. To improve understanding of river-floodplain connectivity in the Lower Rio Grande River region, this study uses a finite-volume, 2D hydrodynamic model with 1-meter high resolution lidar topographic data to better model river-floodplain connectivity in the Lower Rio Grande in correspondence of discharges over a range of return periods. The numerical model is calibrated and validated using historical data collected at a nearby gage station, and metrics are used to quantify river-floodplain connectivity in the Lower Rio Grande. In particular, we aim to identify what locations along the study domain get inundated during different return period events, and quantify the water depths, residence times, and lateral exchange that are associated with the inundation patterns. These quantification metrics allow us to holistically account for the surface water hydrologic connectivity of the floodplain and determine locations along the domain that experience the most connectivity. We found that high vegetative areas and levees influence river-floodplain interaction by acting as buffers along the floodplain. Oxbow lakes and levees systems are also shown to be inundated in 50% of the return periods analyzed. Additionally, we found that residence times are highest at lower return periods, while lateral exchange are highest at high return periods. This inverse behavior indicates that water can ultimately be transported further into the floodplain during larger storm events, while the lower storms can cause the floodplain to experience longer water retention in the floodplain. With some further improvements, these data can be leveraged to inform local stakeholders about the river interaction with the floodplain and integrated in decision-making tools to ultimately develop management strategies that make river systems more resilient to future climate scenarios
Book Synopsis Incorporating Remotely Sensed Data Into Coastal Hydrodynamic Models by : Stephen Conroy Medeiros
Download or read book Incorporating Remotely Sensed Data Into Coastal Hydrodynamic Models written by Stephen Conroy Medeiros and published by . This book was released on 2012 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness. A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation. The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM. Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient. In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas. Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.
