Geospatial Analysis of Winona's Flooding Patterns

Authored by Nicholas Brand

Introduction

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Satellite Imagery GIS311

Geospatial Analysis of Winona's Flooding History

Research Question: What are the most flood-prone areas in Winona, Minnesota, and how have these areas changed over the past decade?

Flooding is a significant natural hazard that affects many communities worldwide, and Winona, Minnesota, is no exception. Nestled along the banks of the Mississippi River, Winona is particularly vulnerable to flooding due to its geographical location within a floodplain. Understanding which areas are most prone to flooding and how these flood-prone areas have evolved over time is crucial for developing effective flood management strategies, protecting property, and ensuring the safety of residents.

This project aims to identify and analyze the most flood-prone areas in Winona, Minnesota, over the past decade. By leveraging advanced geospatial analysis techniques, we will map historical flood events, assess current flood risks, and examine the changes in flood-prone zones. Our goal is to provide actionable insights that can inform future urban planning and flood mitigation efforts.

Studying flood-prone areas is essential for several reasons:

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WC Lidar DEM 3m

  1. Risk Management: Identifying areas at high risk of flooding helps in prioritizing resources and implementing targeted flood prevention measures.
  2. Community Safety: Understanding flood patterns and trends ensures that residents are better prepared for potential flood events.
  3. Urban Planning: Informed decisions regarding land use, infrastructure development, and zoning can reduce the adverse impacts of flooding.
  4. Environmental Protection: Effective flood management can help preserve natural floodplain ecosystems and reduce the environmental damage caused by floods.

By exploring the historical, geographical, and environmental factors contributing to flooding in Winona, this StoryMap will offer a comprehensive view of the city's flood challenges and resilience efforts. Through detailed maps, data analysis, and interactive elements, we aim to engage the audience and foster a deeper understanding of Winona's relationship with the Mississippi River and the ongoing efforts to manage flood risks.

Join us as we delve into the past and present of Winona's flooding history, uncovering the dynamics of flood-prone areas and the strategies employed to safeguard this vibrant community against the forces of nature.

Methodology

Data

  • LiDAR DEM data from MnTOPO at 3m resolution
  • Minnesota Counties Shapefile from Minnesota Geospatial Commons
  • Minnesota Cities Shapefile from Minnesota Geospatial Commons
  • Precipitation Data, 2013 - 2023 from NOAA
  • Flood Data Record, 2022 - 1963 from USGS
  • Minnesota River and Streams from Minnesota Geospatial Commons

Tools and Techniques

Code used for Stitching Precipitation and Flood Data

  • Jupyter Notebooks
    • Purpose: To code a program that stitches precipitation and flood data together.
    • Description: Using Python and relevant libraries (e.g., Pandas, NumPy), write a script to merge and preprocess the precipitation and flood data for analysis.
  • Select Layer By Attribute
    • Purpose: To select Winona County from the Minnesota Counties shapefile.
    • Description: Use the attribute selection tool in ArcGIS Pro to isolate Winona County based on its attribute information.
  • Export Features
    • Purpose: To create a separate shapefile for Winona County.
    • Description: Export the selected Winona County feature to a new shapefile for focused analysis.
  • Clip Layer
    • Purpose: To clip the study area from the LiDAR DEM.
    • Description: Use the clip tool in ArcGIS Pro to extract the DEM data specific to Winona County, ensuring the analysis is confined to the relevant geographic area.
  • Fill
    • Purpose: To fill the basins of the raw DEM file.
    • Description: Apply the Fill tool to the DEM to remove any sinks or depressions, creating a hydrologically correct surface.
  • Flow Direction
    • Purpose: To calculate the flow direction from each cell in the DEM.
    • Description: Use the Flow Direction tool to determine the direction of water flow across the DEM surface.
  • Flow Distance
    • Purpose: To calculate the distance water will flow.
    • Description: Use the Flow Distance tool to compute the distance water will travel across the DEM, aiding in understanding flow pathways.
  • Flow Accumulation
    • Purpose: To calculate the accumulated flow to each cell in the DEM.
    • Description: Apply the Flow Accumulation tool to quantify the amount of water that accumulates in each cell, identifying potential flood areas.
  • Raster Calculator
    • Purpose: To perform map algebra operations.
    • Description: Use the Raster Calculator to conduct various spatial analyses and mathematical operations on the raster data.
  • Con (Conditional)
    • Purpose: To apply conditional statements to raster data.
    • Description: Use the Con tool to create new rasters based on specified conditions, such as identifying areas meeting certain criteria.
  • Raster to Polygon
    • Purpose: To convert raster data to vector polygons.
    • Description: Use the Raster to Polygon tool to transform the raster outputs into polygon features for further analysis and visualization.
  • Minus
    • Purpose: To perform a subtraction operation between two raster datasets.
    • Description: Use the Minus tool to subtract one raster from another, useful for change detection and comparative analysis.

Process

City of Winona Data

  • Select Layer By Attribute (Select Layer By Attribute (2))
    • Input: Counties
    • Expression: CountyName = 'Winona'
    • Output: Winona (Selected Feature)
  • Export Features (Export Features (2))
    • Input: Winona (Selected Feature)
    • Output: Winona County
  • Select Layer By Attribute (Select Layer By Attribute)
    • Input: Cities
    • Expression: CityName = 'Winona'
    • Output: Winona (Selected Feature)
  • Export Features (Export Features)
    • Input: Winona (Selected Feature)
    • Output: City of Winona
  • Clip Layer (Clip Layer)
    • Input: Winona County and City of Winona
    • Output: Winona Flood Polygon
  • Fill Sinks in DEM (Fill)
    • Input: dem_3m_n
    • Output: FillDEM_3m
  • Flow Direction (Flow Direction)
    • Input: FillDEM_3m
    • Output: Flow_Direction
  • Flow Accumulation (Flow Accumulation)
    • Input: Flow_Direction
    • Output: Flow_Accumulation
  • Raster Calculator (Stream) (Raster Calculator)
    • Expression: "Flow_Accumulation" > 100
    • Output: Stream
  • Flow Distance (Flow Distance)
    • Input: FillDEM_3m, Flow_Direction, Stream
    • Output: HAND
  • Con Tool (Flood Extent Minimum) (Con)
    • Input conditional raster: HAND
    • Expression: VALUE <= 13.93
    • Input true raster or constant value: 1
    • Output: Flood_Extent_Maximum
  • Minus Tool (Minus)
    • Input: Flood_Extent_Maximum, HAND
    • Output: Flood Depth
  • Convert Raster to Polygon (Raster to Polygon)
    • Input: Flood_Extent_Maximum
    • Output: Flood Polygon

By following these steps meticulously, you can recreate the analysis and identify flood-prone areas in Winona, Minnesota, just as we did. This process leverages advanced geospatial analysis techniques to provide valuable insights into the changes in flood-prone areas over the past decade.

Step-by-Step Model of Analysis

Flowchart Explanation

The flowchart visually represents the methodology described above. Here's an overview of the components:

  • Inputs (Blue Ovals): Represent the initial datasets and constants used in the analysis with the exception of Constant Values, Counts, and Flow Accumulation.
  • Processing Steps (Yellow Rectangles with Red Stars): Indicate the geoprocessing operations performed, such as Fill, Flow Direction, Flow Accumulation, and Raster Calculator.
  • Outputs (Green Hexagons with Orange Diamonds): Show the results of the processing steps, including Winona County, City of Winona, Winona Flood Polygon, and other derived layers.
  • Constant Values (Purple Circles): Represent the constant values used in the calculations.

By following this detailed methodology and utilizing advanced geospatial analysis techniques, we were able to identify and analyze the flood-prone areas in Winona, Minnesota, providing valuable insights into how these areas have changed over the past decade.

import pandas as pd

def parse_usgs_rdb(file_path):
    with open(file_path, 'r') as file:
        lines = file.readlines()

    # Find the header end
    data_start_idx = 0
    for idx, line in enumerate(lines):
        if not line.startswith("#"):
            data_start_idx = idx
            break

    # Read column names and definitions
    column_names = lines[data_start_idx].strip().split("\t")
    column_definitions = lines[data_start_idx + 1].strip().split("\t")

    # Read the data section
    data = [line.strip().split("\t") for line in lines[data_start_idx + 2:]]

    # Create a DataFrame
    df = pd.DataFrame(data, columns=column_names)

    # Convert datetime column to datetime type
    df['datetime'] = pd.to_datetime(df['datetime'])

    return df

# Path to the rdb file
file_path = "F:/Documents/Schooling/College/GIS 311/Final Story Map Project/Historical_River_Discharge.rdb"

# Parse the rdb file
usgs_df = parse_usgs_rdb(file_path)

# Display the first few rows of the parsed data
print(usgs_df.head())

# Save the cleaned data to a new CSV file
cleaned_file_path = "F:/Documents/Schooling/College/GIS 311/Final Story Map Project/Cleaned_Historical_River_Discharge.csv"
usgs_df.to_csv(cleaned_file_path, index=False)

Winona County Data

This map displays the Minimum Flood Extend in green and the Maximum Flood Extent in Blue with the River visible to the right and Lock & Dams highlighted in red circles.

These are the output maps all displaying slightly different analyses but show the possible flood zones in Winona. MN, showing Winona County

These are the output maps all displaying slightly different analyses but show the possible flood zones in Winona. MN, showing City of Winona

Conclusion

The geospatial analysis conducted in this project offers a comprehensive representation of potential flood-prone areas in Winona, Minnesota. By employing advanced spatial analysis techniques and utilizing detailed data sources such as LiDAR DEM, we have mapped the regions where the altitude is lower than the river level during small floods and the most catastrophic flood in Winona's history.

These maps provide crucial insights into the areas at risk of flooding. They highlight the regions that are potentially vulnerable based on their elevation relative to the river. However, it is essential to contextualize these findings within the historical and infrastructural developments in Winona. Specifically, the catastrophic flood of 1965 prompted the construction of a robust dike and levee system around the entire town. This significant engineering feat has been a critical factor in protecting Winona from subsequent floods. As a result, while the maps indicate potential flood zones, the presence of the levee means that water cannot directly spill into the town as it might have before 1965.

Our initial objective was to analyze how these flood zones have changed over the past decade. The findings reveal that there has been virtually no change in the flood-prone areas. The LiDAR imagery from 2014 was compared with more modern LiDAR data, and the results showed a negligible difference of only 0.02%. This consistency indicates that the flood risk zones have remained stable over the years.

Moreover, the dike and levee system built in 1965 has not undergone significant changes, further contributing to the stability of the flood-prone areas. Although it would be intriguing to compare the current flood risk zones with those from 1965, the earliest available LiDAR data for Minnesota dates back to 2008. This limitation precludes a direct comparison between the pre- and post-levee conditions.

In conclusion, the analysis confirms that the flood-prone areas in Winona have remained largely unchanged over the past decade, thanks in large part to the enduring effectiveness of the levee system. While our maps accurately depict potential flood zones based on topographical data, the presence of the levee is a crucial mitigating factor that significantly alters the practical flood risk for the town. Future studies could benefit from more historical data to provide a deeper understanding of how flood risk has evolved over time. This project underscores the importance of combining historical context with geospatial analysis to develop a holistic understanding of flood risk and management.

City of Winona Analysis Maps

Winona County Analysis Maps

References

Counties and Cities & Townships, Twin Cities Metropolitan Area - Minnesota Geospatial Commons. (n.d.). https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-metro-counties-and-ctus

County Boundaries, Minnesota - Minnesota Geospatial Commons. (n.d.). https://gisdata.mn.gov/dataset/bdry-counties-in-minnesota

Exporting a model to a graphic—ArcMap | Documentation. (n.d.). https://desktop.arcgis.com/en/arcmap/latest/analyze/modelbuilder/exporting-a-model-to-a-graphic.htm

Floodplain delineation from lidar points—ArcMap | Documentation. (n.d.). https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/floodplain-modeling-using-lidar-in-arcgis.htm

Grouping—ARCGIS Pro | Documentation. (n.d.). https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/modelbuilder/grouping.htm

Jeremy Moore, Minnesota Department of Natural Resources. (n.d.). MNTOPO. http://arcgis.dnr.state.mn.us/maps/mntopo/

Mississippi River at Lock and Dam 5A NR Winona, MN. (n.d.). https://waterdata.usgs.gov/monitoring-location/05378490/#parameterCode=00065&period=P7D&showMedian=false

Mississippi River at Lock Winona, MN. (n.d.). https://waterdata.usgs.gov/monitoring-location/05378490/#parameterCode=00065&period=P7D&showMedian=false

Mississippi River below Lock and Dam 5a - Winona. (n.d.). https://water.noaa.gov/gauges/widm5

MNTOPO. (n.d.). Minnesota Department of Natural Resources. https://www.dnr.state.mn.us/maps/mntopo/index.html

Nicholas Brand “Understanding Winona, Minnesota River Flooding.” (2024, June 27). ArcGIS StoryMaps. https://storymaps.arcgis.com/stories/b13cecab5aff4da1b635a9de57c93373

NOAA’s National Weather Service. (n.d.). A detailed look at the Mississippi River flood of 1965. https://www.weather.gov/arx/flood1965_details

NOAA’s National Weather Service. (n.d.). La Crosse climate Summary for 2013. https://www.weather.gov/arx/lse2013

Use Flood Impact Analysis—ARCGIS Solutions | Documentation. (n.d.). https://doc.arcgis.com/en/arcgis-solutions/10.9.1/reference/use-flood-impact-analysis.htm

Use Flood Impact Analysis—ARCGIS Solutions | Documentation. (n.d.). https://doc.arcgis.com/en/arcgis-solutions/10.9.1/reference/use-flood-impact-analysis.htm#ESRI_SECTION1_BCB8E1527E494C87BBF88D1A702FA991

Use ModelBuilder—ArcGIS Pro | Documentation. (n.d.). https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/modelbuilder/modelbuilder-quick-tour.htm

“Simple” Floodplain Delineation using only a LiDAR DEM and Stream layer. (2022, August 16). Esri Community. https://community.esri.com/t5/water-resources-questions/simple-floodplain-delineation-using-only-a-lidar/td-p/1202972

Nicholas Brand

Code used for Stitching Precipitation and Flood Data

Step-by-Step Model of Analysis