Set up a flood simulation

In 2024, the United States experienced 27 weather- and climate-related events that resulted in damage of more than $1 billion each to affected communities and their infrastructure. Severe rainfall and flooding events make up a significant portion of these costs, with Hurricane Helene alone causing $79.6 billion in damage to the southeastern United States. Understanding and preparing for flooding caused by these extreme weather events has become a critical need for communities.

In this two-part tutorial series, you'll use ArcGIS Pro to perform a sophisticated flood simulation for a hypothetical high rainfall event in Houston, Texas. By analyzing the results alongside structure and road data, it's possible to predict how infrastructure might be impacted by such an event.

In this tutorial, the first in the series, you'll prepare the data necessary to perform the simulation. In particular, you'll use land cover and impervious surface data to create a water infiltration raster layer that shows how quickly water enters the soil, with areas of low infiltration being more susceptible to flooding. In the second tutorial, you'll use the data you prepared to simulate flooding and analyze potential impacts to infrastructure.

This tutorial was last tested on April 9, 2026, using ArcGIS Pro 3.6. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro Advanced (see options for software access)

Set up a flood simulation

Explore the data

First, you'll download an ArcGIS Pro project package containing the starting data for the tutorial. Then, you'll explore the data and become familiar with the study area.

1. Download the Houston Flood Simulation 1 project package.
2. Browse to the downloaded file and double-click it to open the project in ArcGIS Pro. If necessary, sign in using your licensed ArcGIS account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   

   The project contains a 3D scene of Houston, Texas, a low-lying city that has experienced significant flooding events in the past. Your study area includes a corner of northwest downtown Houston ahead of the confluence of the Buffalo Bayou and the White Oak Bayou, marshy wetlands prone to major flooding.

   The Contents pane lists the following layers:

   • Structures—Building footprint polygons clipped to the area of interest from the USA Structures layer in ArcGIS Living Atlas of the World. The original source of the data is the Federal Emergency Management Agency (FEMA). The data was modified to add approximate height values (in meters) for 3D visualization purposes. The structures are symbolized by usage type (such as residential, commercial, and governmental).
   • Roads—Road centerlines clipped to the area of interest from the Transportation (Roads and Railroads) layer in ArcGIS Living Atlas. The original source of the data is the United States Census Bureau.
   • Model Boundary—The processing extent for the flood simulation model, approximately 6 square kilometers. Features outside the model boundary do not participate in the simulation, and have also been clipped from the scene view.
   • NLCD 2024 Fractional Impervious Surface—A raster dataset of impervious surface percentages clipped from the USA Annual NLCD - Fractional Impervious Surface layer in ArcGIS Living Atlas. The original source of the data is the United States Geological Survey (USGS). Impervious surfaces are ground surfaces that water cannot infiltrate, such as pavement or structures, and can have a large impact on flooding.
   • USA SSURGO Soil Hydrologic Group—A raster dataset of soil group types in seven classes clipped from the USA SSURGO - Soil Hydrologic Group layer in ArcGIS Living Atlas. The original source of the data is the United States Department of Agriculture (USDA). Like impervious surfaces, soil types have an impact on water infiltration and flooding. You'll remap these soil group types along with the impervious surface data to create a raster layer of numerical infiltration rates for use in the flood simulation.
   • NLCD 2024 Land Cover—A raster dataset of land cover types in 16 classes clipped from the USA Annual NLCD - Land Cover layer in ArcGIS Living Atlas. Later, you'll remap the land cover values in this layer to Manning's surface roughness coefficients, which is used by the flood simulation to quantify friction and energy loss as surface water flows over different land cover types.
   • Elevation—The Terrain 3D elevation surface from ArcGIS Living Atlas, which provides the terrain used for 3D visualization and analysis of the flood simulation. The structures and roads are aligned with or draped on this surface to determine their relative elevations. The data for Houston in this layer comes from the USGS ED Elevation Program 1-meter resolution lidar-derived digital elevation model.
   • The four Environment layers and the World Hillshade layer are part of the Environment Map basemap, accessed from the basemap gallery. They are used for visualization purposes only.

   Note:

   Other than the model boundary, all of the data in this tutorial was clipped from ArcGIS Living Atlas datasets that cover the entire United States. By clipping the same datasets to your own study area, you can perform the workflow in this tutorial for any area of interest in the United States. To learn how to clip data, try the tutorial Clip features to a region. The raster layers were clipped using the Copy Raster tool.

   Next, you'll explore some of the changes that were made to the scene to prepare it for visualization.

3. In the Contents pane, double-click Houston Flood Simulation.

   

   The Map Properties window for the scene appears.

4. Click the Clip Layers tab.

   

   The scene has been clipped to a custom extent. In this case, the extent was defined by the Model Boundary layer. No data will appear outside of this extent in the scene.

5. Close the Map Properties window.

   Another change made to the scene for visualization purposes is that the Structures layer was extruded using the Height attribute. This extrusion gives the building footprints, which are normally flat polygons, a 3D appearance in the scene. Where no height attribute is available, a default height of 4 meters is used so that the structures will provide obstructions to the flow of water.

   Note:

   To learn how to extrude building footprints using a height attribute, try the tutorial Map Venice in 3D.

Calculate roughness coefficients

Now that you're familiar with the data and the visualization techniques applied to the scene, you'll prepare the datasets that you'll later use as inputs for the flood simulation. In addition to the Structures layer, which will be used to represent buildings that obstruct the flow of water, and the Elevation layer, which will be used as the ground surface, the following inputs are needed:

• A surface roughness raster, which represents the level of obstruction of the terrain. You can create this raster by calculating it from the NLCD 2024 Land Cover layer.
• An infiltration rate raster, which represents the speed at which surface water enters the soil. You can create this raster by calculating it from the USA SSURGO Soil Hydrologic Group and NLCD 2024 Fractional Impervious Surface layers.
• A maximum infiltration raster, which represents the water depth threshold at which rainwater is no longer absorbed into the ground and becomes runoff. You can create this raster by calculating it from the NLCD 2024 Land Cover layer.
• The rainfall rate, which you'll determine from NOAA Atlas 14 rainfall estimates for Houston.

First, you'll create the surface roughness raster. Before you do, you'll learn about the NLCD 2024 Land Cover layer in more detail.

1. In the Contents pane, expand the NLCD 2024 Land Cover layer. Check the box to turn the layer on.

   

   The layer contains five land cover classes. One of these classes is open water, while the other four are different densities of developed land.

   Note:

   The original layer from which this layer was clipped contains 16 land cover classes for the conterminous United States for the years 1985 to 2024.

2. Turn off the Structures and Roads layers.

   On the map, the majority of the land cover is one of the four developed classes, which are all various shades of red. As the downtown area of Houston, a major city, most of the study area is developed.

   

   To transform this data into a surface roughness raster, you'll convert, or remap, the individual land cover classes to numeric roughness coefficients, which the flood simulation uses to determine the amount of friction or energy loss experienced as water flows over different types of land cover. You'll perform the conversion with a raster function, which applies processing directly to an imagery dataset (compared to geoprocessing tools, which create a new layer as an output).

3. On the ribbon, click the Analysis tab. In the Raster group, click the Raster Functions button.

   

4. In the Raster Functions pane, click the Project tab.

   

   This tab contains raster functions that were included as part of the project package you downloaded. These raster functions were designed specifically for the workflow in this tutorial.

   Tip:

   To use one of these raster functions in your own flood simulation projects, right-click the function and choose Export. Save the raster function to a location of your choice. To add an exported raster function to a project, point to the project name on the Project tab of the Raster Functions pane and click the Import functions button. Browse to and add the raster function you saved.

5. Point to the Remap Land Cover to Surface Roughness Coefficient raster function.

   

   A tooltip describes the raster function and how it works. The raster function remaps land cover classes to Manning's roughness coefficients.

6. Right-click the Remap Land Cover to Surface Roughness Coefficient raster function and choose Edit.

   

   The Raster Function Editor view appears, showing the function.

   

   This function uses the land cover raster as an input for the Remap raster function, which was configured with custom parameters.

7. Double-click the Remap raster function.

   The Remap Properties window appears, showing the function parameters. The preconfigured parameters include a table of remap values. This table takes each of the 16 land cover classes (listed in the table as rows 1 through 16) and changes them into Manning's roughness coefficient values. These values were taken from the HEC-RAS 2D User's Manual, using the middle of the listed value range.

8. Close the Remap Properties window and the Raster Function Editor view.
9. In the Raster Functions pane, click Remap Land Cover to Surface Roughness Coefficient.

   The function opens.

10. For Raster, choose NLCD 2024 Land Cover.

    

    It's not necessary to adjust the remap table, which is already configured for NLCD land cover classes.

11. Click Create new layer.

    

    The function runs. A new raster, Remap Land Cover to Surface Roughness Coefficient, is added to the scene and the Contents pane.

    

    Areas with lighter colors are those with higher roughness. Because this raster is only intended for use in the simulation calculation, there's no need to adjust its symbology, or the symbology of any of the other input layers. The highly developed downtown area tends to have much higher roughness than the suburban and open space areas.

12. In the Contents pane, turn off and collapse the Remap Land Cover to Surface Roughness Coefficient and NLCD 2024 Land Cover layers.

Calculate infiltration rates

Infiltration rates, or the speed at which surface water enters the soil, can have a big impact on the severity of a flooding event. Increased infiltration rates reduce the amount of surface water present during a flood event, as more water is absorbed into the ground and less becomes runoff. Typically, infiltration rates are expressed in inches or millimeters per hour, and an infiltration rate raster is an essential input in a flood simulation.

The following images show the amount of surface water during a severe 1-hour, 100mm rainfall event with (from left to right) 25mm, 50mm, and 75mm infiltration rates. The higher infiltration rates have less surface water, and thus less flooding.

Although there are many viable methods for creating an infiltration rate raster, you'll create one using the USA SSURGO Soil Hydrologic Group and NLCD 2024 Fractional Impervious Surface layers to approximate local variations in infiltration rates for the area of interest.

1. In the Contents pane, turn on and expand the USA SSURGO Soil Hydrologic Group layer.

   

   This layer shows the different SSURGO soil hydrologic groups present in the area of interest. For Houston, there are three groups, while the original nationwide dataset contains seven groups and group combinations. These groups describe how quickly rainfall is absorbed in the soil.

   Similar to how you created the surface roughness coefficient raster, you'll remap these soil groups to infiltration rates (in millimeters per hour) based on the middle values in the ranges in the HEC-RAS 2D User's Manual. However, soil alone doesn't influence infiltration rates, so after the raster is remapped, it'll be further adjusted based on impervious surfaces in each area.

2. Turn off and collapse the USA SSURGO Soil Hydrologic Group layer. Turn on and expand the NLCD 2024 Fractional Impervious Surface layer.

   

   This layer shows the relative percentage of impervious surfaces throughout the study area. Highly developed and paved areas are darker and have more than 80 percent impervious surfaces. Lighter open spaces have less than 20 percent impervious surfaces, while suburban neighborhoods have values in between. Higher percentages of impervious surfaces will reduce infiltration rates, since water will not be absorbed by them.

3. Turn off and collapse the NLCD 2024 Fractional Impervious Surface layer.

   You'll use another raster function to calculate infiltration rates based on these layers.

4. In the Raster Functions pane, point to the Remap Soil Hydrologic Groups and Impervious Surfaces to Infiltration Rates raster function to learn more about it.

   

5. Right-click the raster function and choose Edit.

   The Raster Function Editor view appears, showing the components of the function.

   

   Because this raster function combines two layers, it has more components than the previous one. It performs the following actions:

   • Use the Remap function to convert the SSURGO soil hydrologic groups to infiltration rates in mm/hour.
   • Use the Divide, Minus, and Abs functions to convert the impervious surface values (expressed as percentages) to an impervious conversion factor (expressed as a number between 0 and 1). For example, a pixel that is 20 percent impervious will be converted to a factor of 0.8.
   • Use the Times function to multiply the two outputs together, creating an infiltration rates raster that accounts for both soil hydrologic group and impervious surfaces.
6. Close the Raster Function Editor view. In the Raster Functions pane, click the Remap Soil Hydrologic Groups and Impervious Surfaces to Infiltration Rates raster function.
7. For Fractional Impervious Surface Raster, choose NLCD 2024 Fractional Impervious Surface. For SSURGO Soil Hydrologic Group Raster, choose USA SSURGO Soil Hydrologic Group.

   

8. Click Create new layer.

   The Remap Soil Hydrologic Groups and Impervious Surfaces to Infiltration Rates layer is added to the scene and the Contents pane.

   

   Darker areas have lower infiltration rates. As before, there's no need to change the symbology.

9. Turn off and collapse the Remap Soil Hydrologic Groups and Impervious Surfaces to Infiltration Rates layer.

Calculate maximum infiltration

The final input for the flood simulation is the maximum infiltration layer. This layer determines the water depth threshold in millimeters at which rainwater is no longer absorbed into the ground and becomes runoff. You'll calculate this raster using the NLCD 2024 Land Cover raster, which you also used to calculate the roughness coefficients.

1. In the Raster Functions pane, point to Remap Land Cover to Maximum Infiltration to read more about the raster function.

   

2. Right-click the raster function and choose Edit.

   The function appears in the Raster Function Editor view. Similar to the surface roughness coefficients function, this function only uses the Remap function.

3. Close the Raster Function Editor view. In the Raster Functions pane, click Remap Land Cover to Maximum Infiltration.
4. For Raster, choose NLCD 2024 Land Cover.

   

5. Click Create new layer.

   The Remap Land Cover to Maximum Infiltration layer is added to the scene and the Contents pane.

   

   Darker areas have lower maximum infiltration. Like many of the result layers, heavily developed areas such as downtown Houston have attributes that make them more susceptible to flooding compared to less developed suburban or unbuilt areas.

6. Close the Raster Functions pane.
7. Turn off and collapse the Remap Land Cover to Maximum Infiltration layer. Turn on the Structures and Roads layers.

   Before you continue, you'll rename the layers that you'll use in the flood simulation to have shorter names that will make them quicker to identify.

8. In the Contents pane, click Remap Land Cover to Maximum Infiltration to select it.
9. Click the layer name to make it editable. Rename it to Maximum Infiltration and press Enter.
10. Rename Remap Soil Hydrologic Groups and Impervious Surfaces to Infiltration Rates to Infiltration Rates. Rename Remap Land Cover to Surface Roughness Coefficient to Surface Roughness.

    

11. On the Quick Access Toolbar, click the Save Project button.

    

You've now set up a project to run a flood simulation for the area in and around downtown Houston. You have all the necessary input layers, including layers of infrastructure such as structures and roads, an elevation layer, and the three raster layers you created using raster functions: surface roughness coefficients, infiltration rates, and maximum infiltration.

In the next tutorial in this series, you'll use these input layers to simulate a flood and assess the potential damage to infrastructure.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The Structures layer was derived from the USA Structures layer.
• The Roads layer was derived from the Transportation (Roads and Railroads) layer.
• The NLCD 2024 Fractional Impervious Surface layer was derived from the USA Annual NLCD - Fractional Impervious Surface layer.
• The USA SSURGO Soil Hydrologic Group layer was derived from the USA SSURGO - Soil Hydrologic Group layer.
• The NLCD 2024 Land Cover layer was derived from the USA Annual NLCD - Land Cover layer.
• The Model Boundary layer was created by the tutorial author.
• Environment Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, © World Wildlife Fund, Inc., and the GIS User Community