InVEST documentation

Urban Flood Risk Mitigation model


Flood hazard comes from different sources, including: riverine (or fluvial) flooding, coastal flooding, and stormwater (or urban) flooding - the focus of this InVEST model. Natural infrastructure can play a role for each of these flood hazards. Related to stormwater flooding, natural infrastructure operates mainly by reducing runoff production, slowing surface flows, and creating space for water (in floodplains or basins).

The InVEST model calculates the runoff reduction, i.e. the amount of runoff retained per pixel compared to the storm volume. For each watershed, it also calculates the potential economic damage by overlaying information on flood extent potential and built infrastructure.

The model

How it works

Runoff production and runoff attenuation index

For each pixel \(i\), defined by a land use type and soil characteristics, we estimate runoff \(Q\) (mm) with the Curve Number method:

(95)\[\begin{split}Q_{p,i} = \begin{Bmatrix} \frac{(P - \lambda S_{max_i})^2}{P + (1-\lambda) S_{max,i}} & if & P > \lambda \cdot S_{max,i} \\ 0 & & otherwise \end{Bmatrix}\end{split}\]

Where \(P\) is the design storm depth in mm, \(S_{max,i}\) is the potential retention in mm, and \(\lambda \cdot S_{max}\) is the rainfall depth needed to initiate runoff, also called the initial abstraction (\(\lambda=0.2\) for simplification).

\(S_{max}\) (calculated in mm) is a function of the curve number, \(CN\), an empirical parameter that depends on land use and soil characteristics (NRCS 2004):


The model then calculates runoff retention per pixel \(R_i\) as:


And runoff retention volume per pixel \(R\_m3_i\) as:

(98)\[R\_m3_i=R_i\cdot P\cdot pixel.area\cdot 10^{-3}\]

With \(pixel.area\) in \(m^2\).

Runoff volume (also referred to as “flood volume”) per pixel \(Q\_m3_i\) is also calculated as:

(99)\[Q\_m3_i=Q_{p,i}\cdot pixel.area\cdot 10^{-3}\]

Calculate potential service (optional)

The service is the monetary valuation of avoided damage to built infrastructure and number of people at risk. As of this version of InVEST, the population metrics described here are not yet implemented.

For each watershed (or sewershed) with flood-prone areas, compute:

  • Affected.Pop : total potential number of people affected by flooding (could focus on vulnerable groups only, e.g. related to age, language, etc. See Arkema et al., 2017, for a review of social vulnerability metrics). This metric is calculated by summing the population in the intersection of the two shapefiles (watershed and flood-prone area).

  • \(Affected.Build\) : sum of potential damage to built infrastructure in $, This metric is calculated by multiplying building footprint area within the watershed and potential damage values in \(m^2\).

Aggregation of runoff retention and potential service values at the watershed scale

For each watershed, compute the following indicator of the runoff retention service:


where \(pixel.area\) is the pixel area (\(m^2\)), \(Service.built\) is expressed in \(m^3\).

Limitations and simplifications

Runoff production: the model uses a simple approach (SCS-Curve Number), which introduces high uncertainties. However, the ranking between different land uses is generally well captured by such an approach, i.e. that the effect of natural infrastructure will be qualitatively represented in the model outputs. Future work will aim to include a routing over the landscape: ideas include TOPMODEL (there is an R package), UFORE (used in iTree), CADDIES, etc

Valuation approaches: Currently, a simple approach to value flood risk retention is implemented, valuing flood risk as the avoided damage for built infrastructure. Alternative approaches (e.g. related to mortality, morbidity, or economic disruption) could be implemented.

Data needs

  • Workspace (required): Folder where model outputs will be written. Make sure that there is ample disk space, and write permissions are correct.

  • Suffix (optional). Text string that will be appended to the end of output file names, as “_Suffix”. Use a Suffix to differentiate model runs, for example by providing a short name for each scenario. If a Suffix is not provided, or changed between model runs, the tool will overwrite previous results.

  • Watershed Vector (required). shapefile delineating areas of interest, which should be hydrologic units: watersheds or sewersheds.

  • Depth of rainfail in mm (required). This is \(P\) in equation (95). Also see Table 1 in Appendix, below.

  • Land Cover Map (required). Raster of land use/land cover (LULC) for each pixel, where each unique integer represents a different land use/land cover class. All values in this raster MUST have corresponding entries in the Land Cover Biophysical Table. The model will use the resolution of this layer to resample all outputs. The resolution should be small enough to capture the effect of green areas in the landscape, although LULC categories can comprise a mix of vegetated and non-vegetated covers (e.g. “residential”, which may have 30% canopy cover, and have biophysical table parameters that change accordingly)

  • Soils Hydrological Group Raster (required). Raster of categorical hydrological groups. Pixel values must be limited to 1, 2, 3, or 4, which correspond to soil hydrologic group A, B, C, or D, respectively (used to derive the CN number)

  • Biophysical Table (required). A .csv (Comma Separated Value) table containing model information corresponding to each of the land use classes in the Land Cover Map. All LULC classes in the Land Cover raster MUST have corresponding values in this table. Each row is a land use/land cover class and columns must be named and defined as follows:

    • lucode: Land use/land cover class code. LULC codes must match the value column in the Land Cover Map raster and must be integers and unique.

    • Curve number (CN) values for each LULC type and each hydrologic soil group. Column names should be: CN_A, CN_B, CN_C, CN_D, which the letter suffix corresponding to the hydrologic soil group

  • Built Infrastructure Vector (optional): shapefile with built infrastructure footprints. The attribute table must contain a column ‘Type’, with integers referencing the building type (e.g. 1=residential, 2=office, etc.)

  • Damage Loss Table (optional): Table with columns “Type” and “Damage” with values of built infrastructure type (see above) and potential damage loss (in $/\(m^2\))

Interpreting outputs

The following is a short description of each of the outputs from the urban flood risk mitigation model. Final results are found within the user defined Workspace specified for this model run. “Suffix” in the following file names refers to the optional user-defined Suffix input to the model.

  • Parameter log: Each time the model is run, a text (.txt) file will be created in the Workspace. The file will list the parameter values and output messages for that run and will be named according to the service, the date and time. When contacting NatCap about errors in a model run, please include the parameter log.

  • Runoff_retention.tif: raster with runoff retention values (no unit, relative to precipitation volume). Calculated from equation (97).

  • Runoff_retention_m3.tif: raster with runoff retention values (in \(m^3\)). Calculated from equation (98).

  • Q_mm.tif: raster with runoff values (mm). Calculated from equation (95).

  • flood_risk_service.shp: Shapefile with results in the attribute table:

    • rnf_rt_idx: average of runoff retention values (\(R_i\)) per watershed

    • rnf_rt_m3: sum of runoff retention volumes (\(R\_m3_i\)), in \(m^3\), per watershed.

    • flood_vol: The flood volume (Q_m3, equation (99)) per watershed.

    • aff_bld: potential damage to built infrastructure in $, per watershed. Only calculated when the Built Infrastructure Vector input is provided.

    • serv_blt: \(Service.built\) values for this watershed (see equation (100)). An indicator of the runoff retention service for the watershed. Only calculated when the Built Infrastructure Vector input is provided.

Appendix: Data sources and guidance for parameter selection

The following table summarizes possible data sources for inputs specific to the urban flood risk mitigation model. Additional information on common InVEST inputs (e.g. LULC, evapotranspiration) can be found in the annual water yield model documentation.

Table 1



Depth of rainfall

Depth of rainfall event of interest (mm). To calculate the design storm, users can look up intensity-frequency-duration (IFD) tables available for their city. The storm duration is equal to the average time of concentration of the studied watersheds. Time of concentration can be derived from existing studies or from web tools: eg. See Balbi et al. (2017) for a detailed description of these methods.

Soil Hydrological Group

Two global layers of hydrologic soil group are available, 1) from FutureWater (available at: and 2) ORNL-DAAC’s HYSOGs250m (available at

The FutureWater raster provides numeric group values 1-4 14, 24 and 34. The Urban Flood Risk model requires only values of 1/2/3/4, so you need to convert any values of 14, 24 or 34 into one of the allowed values.

HYSOGs250m provides letter values A-D, A/D, B/D, C/D and D/D. For use in this model, these letter values must be translated into numeric values, where A = 1, B = 2, C = 3 and D = 4. Again, pixels with dual values like A/D, B/D etc must be converted to a value in the range of 1-4.

If desired, soil groups may also be determined from hydraulic conductivity and soil depths. FutureWater’s Soil Hydraulic Properties dataset also contains hydraulic conductivity, as may other soil databases. Table 2 below can be used to convert soil conductivity into soil groups.

In the United States free soil data is available from the U.S. Department of Agriculture’s NRCS gSSURGO, SSURGO and gNATSGO databases: They also provide ArcGIS tools (Soil Data Viewer for SSURGO and Soil Data Development Toolbox for gNATSGO) that help with processing these databases into spatial data that can be used by the model. The Soil Data Development Toolbox is easiest to use, and highly recommended if you use ArcGIS and need to process U.S. soil data.

Biophysical table

It is recommended to do a literature search to look for values for that are specific to the area you’re working in. If these are not available, look for values that correspond as closely as possible to the same types of land cover/soil/climate. If none of these more local values are available, several general sources are recommended. Curve numbers (fields CN_A, CN_B, CN_C, CN_D) can be obtained from the USDA handbook: (NRCS-USDA, 2007 Chap. 9) For water bodies and wetlands that are connected to the stream, CN can be set to 99 (i.e. assuming that those pixels rapidly convey quickflow.) Since the focus is on potential flood effects, CN can be selected to reflect wet antecedent runoff conditions: CN values should then be converted to ARC-III conditions, as per Chapter 10 in NRCA-USDA guidelines (2004).

Areas of interest (Subwatersheds or sewersheds )

Subwatershed can be delineated from the digital elevation model using the InVEST RouteDEM tool. Sewershed data may be available from local municipalities.

Built infrastructure (optional)

Built infrastructure may be obtained from local municipalities or OpenStreetMap data

Potential damage loss for each building type (optional)

In the US, HAZUS provides damage data. Globally, a recent report from the European Commission provides useful data:

Table 2

Group A

Group B

Group C

Group D

Saturated hydraulic conductivity of the least transmissive layer when a water impermeable layer exists at a depth between 50 and 100 centimeters

> 40 micrometers/second

[40 - 10] micrometers/second

[10 - 1] micrometers/second

< 1 micrometer/second (or depth to impermeable layer < 50cm or water table < 60cm)

Saturated hydraulic conductivity of the least transmissive layer when any water impermeable layer exists at a depth greater than 100 centimeters

> 10 micrometers/second

[4 - 10] micrometers/second

[0.4 - 4] micrometers/second

< 0.4 micrometers/second


NRCS-USDA Part 630 Hydrology National Engineering Handbook, Chapter 7 Hydrologic Soil Groups. 2007.

NRCS-USDA Part 630 Hydrology National Engineering Handbook, Chapter 9 Hydrologic Soil-Cover Complexes. 2004.

NRCS-USDA Part 630 Hydrology National Engineering Handbook, Chapter 10 Estimation of Direct Runoff from Storm Rainfall. 2004.