Federally Overlooked Flood Risk Inequities in Houston, Texas: Novel Insights Based on Dasymetric Mapping and State-of-the-Art Flood Modeling

Flores, Aaron B.; Collins, Timothy W.; Grineski, Sara E.; Amodeo, Mike F.; Porter, Jeremy R.; Sampson, Chris; Wing, Oliver

doi:10.1080/24694452.2022.2085656

Cited by 10 publications

(6 citation statements)

References 76 publications

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“…Based on the vulnerability index analysis, it was found that flood vulnerability has a high index in areas that fall within settlements (Figure 10). This model is consistent with the high population density in urban areas due to the dasymetric method [36]. The areas with the highest vulnerability were also concentrated in Pekalongan City, which aligns with their physical vulnerability level.…”

Section: Spatial Model Of Coastal Flood Vulnerabilitysupporting

confidence: 84%

“…In this research, spatial modeling of social vulnerability refers to population density using the dasymetric method. Dasymetric results better represent the distribution of the population exposed to disasters due to the consideration of delimitation boundaries in the form of settlements [36]. The data used for analysis is WorldPop, which was accessed through GEE.…”

Section: Social Vulnerabilitymentioning

confidence: 99%

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Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

Siddiq,

Sejati

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The intensity of development in coastal areas stimulates various potential issues such as flood disasters. This study aims to demonstrate the importance of latest methods and geospatial data as inputs for coastal spatial planning policies in efforts to reduce flood disaster risks. Leveraging spatial analysis with cloud computing through Google Earth Engine (GEE), this research assesses flood risk components—hazards, vulnerability, and capacity. The method involves processing SAR Sentinel-1 data to map flood inundation as a representation of hazards, analyzing Landsat and WorldPop data to evaluate vulnerability, and assessing capacity by utilizing VIIRS nighttime light level imagery to determine economic activities. The chosen research study location is the coastal area of Pekalongan due to frequent flood disasters throughout the year. The results demonstrate that cloud computing is capable of assessing flood risks. The flood inundation model using SAR data covers an area of 2,780 hectares with an accuracy of 96.75%. The analysis also reveals the highest vulnerability level, reaching 15.7% (946.32 hectares) of the total area. The capacity analysis indicates a medium to high level of 15% (913.6 hectares). The assessment of flood risks in the coastal area is dominated by the medium to very high-risk class, covering 43% (2,631.84 hectares) of the area. In conclusion, integrating cloud-based flood risk modeling into spatial planning is crucial, considering disaster resilience for sustainable human habitats.

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Section: Spatial Model Of Coastal Flood Vulnerabilitysupporting

confidence: 84%

Section: Social Vulnerabilitymentioning

confidence: 99%

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

Siddiq,

Sejati

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…More frequent and extreme flooding exacerbated by climate change increases societal impacts that disproportionately affect marginalized populations (e.g., Douglas et al., 2008). Institutions and policies also contribute to heightened flood risk as outdated flood maps and deficient flood risk disclosures drive development in flood‐prone areas (Andreadis et al., 2022; Flores et al., 2022; Hino & Burke, 2021). In addition to poor urban flood governance, flood‐adapted urbanization can intensify risk for communities that cannot access, benefit from, or are even harmed by such development (Ajibade, 2017).…”

Section: Introductionmentioning

confidence: 99%

Earth Observation to Address Inequities in Post‐Flood Recovery

Friedrich,

Tellman,

Sullivan

et al. 2024

Earth's Future

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Floods impact communities worldwide, resulting in loss of life, damaged infrastructure and natural assets, and threatened livelihoods. Climate change and urban development in flood‐prone areas will continue to worsen flood‐related losses, increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood‐hazard monitoring and provide insights on location, timing, and extent in near real‐time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring distributional effects within communities. EO data are uniquely positioned to monitor post‐flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We encourage the EO and flood research community to refocus on developing flood recovery applications to address growing risk. Translation of EO insights on flood recovery among flood‐affected communities and decision‐makers is necessary to address underlying social vulnerabilities that exacerbate inequitable recovery outcomes and advocate for redressing injustices where disparate recovery is observed. We identify an unequivocal need for EO to move beyond mapping flood hazard and exposure toward post‐flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework for remote sensing scientists to engage community‐based partners to integrate EO with non‐EO data to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.

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“…The existing approaches for rating urban flood risk are incomplete, inaccurate, and unreliable 1,12 . In the United States, Flood Insurance Rate Maps (FIRMs) provided by Federal Emergency Management Agency (FEMA) are usually used to analyze urban flood risk 1 .…”

Section: Introductionmentioning

confidence: 99%

“…To obtain the initial target distribution 𝑃, the DNN module is first pre-trained to obtain an initial node embedding 𝑯 (𝐿 𝐺𝐸 ) by minimizing 𝐿 𝑟𝑒𝑠 , as shown in Equation (12). 𝐾 -means clustering is conducted once to obtain the initial clustering centers 𝑢 on the node embedding 𝑯 (𝐿 𝐺𝐸 ) before training the whole clustering model.…”

mentioning

confidence: 99%

Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas

Mostafavi,

kai

2024

Preprint

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Urban flood risk emerges from complex and nonlinear interactions among multiple features related to flood hazard, flood exposure, and social and physical vulnerabilities, along with the complex spatial flood dependence relationships. Existing approaches for characterizing urban flood risk, however, are primarily based on flood plain maps, focusing on a limited number of features, primarily hazard and exposure features, without consideration of feature interactions or the dependence relationships among spatial areas. To address this gap, this study presents an integrated urban flood-risk rating model based on a novel unsupervised graph deep learning model (called FloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence among areas and complex and nonlinear interactions among flood hazards and urban features for specifying emergent flood risk. Using data from multiple metropolitan statistical areas (MSAs) in the United States, the model characterizes their flood risk into six distinct city-specific levels. The model is interpretable and enables feature analysis of areas within each flood-risk level, allowing for the identification of the three archetypes shaping the highest flood risk within each MSA. Flood risk is found to be spatially distributed in a hierarchical structure within each MSA, where the core city disproportionately bears the highest flood risk. Multiple cities are found to have high overall flood-risk levels and low spatial inequality, indicating limited options for balancing urban development and flood-risk reduction. Relevant flood-risk reduction strategies are discussed considering ways that the highest flood risk and uneven spatial distribution of flood risk are formed. The model enables characterizing urban flood risk as an emergent property arising from non-linear interactions among flood hazard and urban features, and integration of spatial flood dependence relationships among areas. Accordingly, this study’s outcomes provide a novel perspective and better understanding of urban flood-risk profiles by harnessing machine intelligence and urban big data.

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Federally Overlooked Flood Risk Inequities in Houston, Texas: Novel Insights Based on Dasymetric Mapping and State-of-the-Art Flood Modeling

Cited by 10 publications

References 76 publications

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

Earth Observation to Address Inequities in Post‐Flood Recovery

Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas

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