New Orleans and Flooding

Research Goals

Research Goals

The primary objective of this study is to assess the vulnerability of New Orleans, Louisiana, to flooding events, with a specific focus on the following aspects:

  1. Evaluating the City’s Susceptibility: Analyze the geographical, environmental, and infrastructural factors that contribute to flood risks in New Orleans.
  2. Assessment of Current Anti-Flooding Systems: Examine the effectiveness of existing flood mitigation systems, focused majorly on the drainage networks and the distribution of grocery stores, to determine their adequacy in handling extreme weather events.
  3. Understanding Socioeconomic Influences: Investigate how socioeconomic disparities, such as income levels, housing locations or population density, and accessibility, influence the city’s flood resilience.
  4. Preparing for Future Flooding Events: Provide actionable insights and recommendations to help residents, policymakers, and urban planners prepare for and mitigate the impacts of future flooding.

The accompanying first map illustrates the location of Orleans Parish within Louisiana. The second map set the spatial context for this research. Both images are captured from the official website of the City of New Orleans.

Background

Background

Hurricane Katrina in 2005 was one of the most devastating natural disasters in New Orleans’ history. Beyond the destruction caused by the flooding, it exposed the city’s vulnerabilities to recurring environmental hazards that continue to pose significant threats to its infrastructure, environment, and communities. The following table highlights the natural hazards with the highest probability of recurrence in New Orleans, as identified by the Hazard Mitigation Plan of the City of New Orleans (https://ready.nola.gov/hazard-mitigation/hazards/summary/). The annual recurrence probabilities for each hazard were calculated using data from the National Oceanic and Atmospheric Administration’s (NCDC/NCEI) database. While this dataset has certain limitations, it represents the most reliable resource currently available.

Data

Floodind Zone

National Flood Hazard Layer (NFHL) – A digital database containing the flood hazard mapping information from FEMA’s National Flood Insurance Program (NFIP).

  • High Risk Flood Areas have a 1 in 4 chance of flooding during a 30-year mortgage, otherwise known as a 1% annual flood risk. “A” areas are typically located near ponds, streams, and rivers. These features increase the likelihood of flood waters damaging the surrounding area. Homes and properties within these areas are subject to mandatory flood insurance purchase requirements.

  • High Risk Coastal Areas: Coastal areas are often the most hazardous of all flood zone areas.

About 20.5% of the total area is classified as being at high risk of flooding, emphasizing the vulnerability of specific zones to water-related disasters. This data highlights the need for targeted flood management strategies in these high-risk areas to safeguard infrastructure, minimize economic losses, and protect vulnerable populations.

Elevation and Slope

DEM Data – USGS 2021 Greater New Orleans 1 meter DEM

  • Areas with a low slope and minimal elevation changes are more vulnerable to flooding, as water tends to accumulate in these regions with limited natural drainage. The DEM data helps identify these vulnerable areas, providing valuable insights for flood risk assessment and mitigation planning.

Land Cover

Land Cover Data – 2017 NOAA OCM High Res Land Cover, 1m

  • Soil and vegetation-covered areas can absorb water, reducing the risk of flooding. In contrast, impervious surfaces such as paved roads and bare ground prevent water infiltration, increasing the likelihood of flooding in these areas.
  • We reclassified the land cover data on a scale of 0 to 5, based on each surface type’s potential for water absorption..

Canals - Drainage Syatem

Canal Network, with varying widths

  • Canals can help mitigate flooding risks by channeling excess water away from vulnerable areas.
  • Wider canals typically have a greater capacity for drainage, improving their ability to handle large volumes of water during heavy rainfall or storm surges.

Method & Analysis

Evaluate the impact of factors on flooding risk using logistic regression.


Factors:

  • Elevation
  • Slope
  • Land Cover Absorption
  • Distance to Canals

Steps

  • Generate 1000 random sample points
    • Points were generated within the main urban areas of the city, focusing on regions with significant flood risk.
    • Approximately 90% of the urban area is covered by low-absorption land types, such as impervious surfaces, which contribute to increased flooding risks. These points help ensure a comprehensive spatial analysis of flood-prone areas.

  • Extract multi values to points:
    • Elevation (-5 to 9)
    • Slope (0–74 degrees)
    • Land Cover Absorption (0–5)
    • Distance to the nearest canal (meters)
    • Flooding Index (1 = Flooded, 0 = Not Flooded)
  • Logistic regression
    • Dependent variable: Flooding Index
    • Independent variables:
      • Normalized values of elevation, slope, land cover absorption, and distance to the nearest canal. These variables are scaled between 0 and 100 to ensure consistency in the analysis.
    • Logistic regression is used for predicting the likelihood of flooding based on the interaction of these variables, helping to identify key factors that contribute to flood vulnerability.

Results

Spatial Coverage of Canal

  • Weighted buffers
    • The weighted buffer approach shows that the canal system covers most of the area within the study region, highlighting its critical role in managing water flow and mitigating flood risks.
    • However, areas with low elevation and minimal slope changes remain at a high risk of flooding, even in proximity to large canals. This suggests that canal coverage alone is insufficient to address flood risks in these zones, and additional drainage infrastructure or flood management strategies may be needed.
    • The map visualizes the extent of canal coverage, with varying canal widths represented in the legend. Wider canals are likely to have greater drainage capacity, but their effectiveness may be limited in low-lying areas where water tends to accumulate.

Logistic Regression

Dependent Variable: Flooding
Variable Coef. Std. Error p-value Significance
(Intercept) -0.814 0.256 0.001 ***
Factor 1: Elevation -0.030 0.008 0.000 ***
Factor 2: Slope 0.010 0.008 0.206
Factor 3: Land Cover Absorption 0.001 0.003 0.696
Factor 4: Distance to Canals 0.016 0.005 0.001 ***

Predictors of Flooding Risk

  • Elevation and Distance to canal are two significant predictors of flooding risk.
  • Land Cover: Limited presence of high-absorption land cover types in the urban area.
  • Slope: Urban environments introduce abrupt changes in slope (e.g., curbs, building edges), creating dispersed slope values that don’t correlate well with natural flooding patterns.
  • Flooding typically occurs in low-elevation, flat areas, where water accumulates due to limited drainage capacity.


Exponent Coefficients
Variable Exp(Coefficient)
Factor 1: Elevation 0.970
Factor 2: Slope 1.010
Factor 3: Land Cover Absorption 1.001
Factor 4: Distance to Canals 1.016
  • Elevation: An odds ratio of 0.97 means a one-unit increase in normalized DEM decreases the odds of flooding by 3%.
  • Distance to Canals: An odds ratio of 1.016 means a one-unit increase in distance from the canal increases the odds of flooding by 1.6%.

Conclusion

Conclusion

  • Elevation and Distance to Canal: These are two of the most significant predictors of flooding risk. Areas with lower elevation and greater distances from canals are more prone to flooding due to reduced water flow and drainage capabilities.

  • Role of Canals: Canals play a crucial role in mitigating flooding by providing pathways for water drainage. However, their effectiveness is often limited in low-elevation zones where drainage systems struggle to cope with water accumulation.

  • Recommendations: Expanding the canal network, upgrading existing drainage infrastructure, and integrating green infrastructure (e.g., permeable surfaces and retention basins) are essential strategies to address current flooding challenges.

  • Access to Resources: Access to flood mitigation resources remains uneven across communities and can still be improved.

Future Work

  • Satellite Image Issues: The satellite imagery currently used in the project is not functioning as expected, potentially due to processing errors or resolution limitations. Addressing these technical issues will improve visualization and analysis.

  • Flooding Data Challenges: High-resolution flooding data from FEMA often causes software crashes when attempting to visualize or process the data using tools like tmap or ggplot. Lowering resolution or exploring alternative visualization tools may be necessary.

  • Incorporating Additional Factors: Future analyses should consider additional variables such as vegetation cover, land use, and flow accumulation to gain a more comprehensive understanding of flooding risks and improve prediction accuracy.