Assessment Configuration

These fields specify settings used to run an assessment.

Required Datasets

Paths to preprocessed datasets that are required to run an assessment. Most users will not need these settings as the assessment will automatically detect datasets in the preprocessed folder. Use these fields if a preprocessed dataset is located in a different folder.

perimeter_p
Type:
str | Path
Default:
r"perimeter"

The path to the preprocessed fire perimeter raster.

CLI option: --perimeter-p

Python kwarg: perimeter_p

dem_p
Type:
str | Path
Default:
r"dem"

The path to the preprocessed DEM raster.

CLI option: --dem-p

Python kwarg: dem_p

dnbr_p
Type:
str | Path
Default:
r"dnbr"

The path to the preprocessed dNBR raster.

CLI option: --dnbr-p

Python kwarg: dnbr_p

severity_p
Type:
str | Path
Default:
r"severity"

The path to the preprocessed burn severity raster.

CLI option: --severity-p

Python kwarg: severity_p

kf_p
Type:
str | Path
Default:
r"kf"

The path to the preprocessed KF-factor raster.

CLI option: --kf-p

Python kwarg: kf_p

Optional Masks

Paths to optional data masks used to implement an assessment. Most users will not need these settings as the assessment will automatically detect datasets in the preprocessed folder. The most common use of these fields is to disable the use of a preprocessed raster. Do this by setting the field’s value to None.

Example:

# Run assessment without an exclusion mask
excluded_p = None
retainments_p
Type:
str | Path
Default:
r"retainments"

The path to the preprocessed retainment feature mask.

CLI option: --retainments-p

Python kwarg: retainments_p

excluded_p
Type:
str | Path
Default:
r"excluded"

The path to the preprocessed excluded area mask.

CLI option: --excluded-p

Python kwarg: excluded_p

included_p
Type:
str | Path
Default:
r"included"

The path to the preprocessed included area mask.

CLI option: --included-p

Python kwarg: included_p

iswater_p
Type:
str | Path
Default:
r"iswater"

The path to the preprocessed water mask.

CLI option: --iswater-p

Python kwarg: iswater_p

isdeveloped_p
Type:
str | Path
Default:
r"isdeveloped"

The path to the preprocessed development mask.

CLI option: --isdeveloped-p

Python kwarg: isdeveloped_p

DEM Units

dem_per_m
Type:
float
Default:
1

The number of DEM elevation units per meter. Use this setting when the DEM has units other than meters.

Example:

# Run assessment for a DEM measured in feet
dem_per_m = 3.28

CLI option: --dem-per-m

Python kwarg: dem_per_m

Delineation

Settings used to delineate the initial stream segment network.

min_area_km2
Type:
float
Default:
0.025

The minimum catchment area in square kilometers (km²). Pixels with smaller catchments will not be used to delineate stream segments.

CLI option: --min-area-km2

Python kwarg: min_area_km2

min_burned_area_km2
Type:
float
Default:
0.01

The minimum burned catchment area in square kilometers (km²). Pixels outside of the fire perimeter with less burned catchment area will not be used to delineate stream segments.

CLI option: --min-burned-area-km2

Python kwarg: min_burned_area_km2

max_length_m
Type:
float
Default:
500

The maximum allowed stream segment length in meters. Stream segments longer than this length will be split into multiple segments.

CLI option: --max-length-m

Python kwarg: max_length_m

Filtering

Settings used to filter the stream segment network.

max_area_km2
Type:
float
Default:
8

Maximum catchment area in square kilometers (km²). Segments whose catchments exceed this size are considered to have flood-like behavior, rather than debris flow-like behavior. These segments will be removed from the network unless they intersect an included area mask.

Example:

# Discard segments with catchments over 8 km2
max_area_km2 = 8

CLI option: --max-area-km2

Python kwarg: max_area_km2

max_exterior_ratio
Type:
float
Default:
0.95

Maximum proportion of catchment outside the fire perimeter (from 0 to 1). Used to determine whether segments are considered in the fire perimeter. If a segment’s catchment is greater than or equal to this value, then the segment is considered outside the perimeter. Set this parameter to 0 to require all segments to pass the physical filtering criterion.

Examples:

# Set the threshold to 95% within the perimeter
max_exterior_ratio = 0.95

# Require all segments to pass the physical criterion
max_exterior_ratio = 0

CLI options: --max-exterior-ratio, --filter-in-perimeter

Python kwarg: max_exterior_ratio

min_slope
Type:
float
Default:
0.12

The minimum average slope gradient along the stream segment. Used to check if a stream segment is sufficiently steep. A segment will fail the check if its average slope gradient is less than this value.

Example:

# Require a slope of at least 12%
min_slope = 0.12

CLI option: --min-slope

Python kwarg: min_slope

min_burn_ratio
Type:
float
Default:
0.25

The minimum proportion of burned catchment area (from 0 to 1). Used to check if a segment is sufficiently burned. A segment will fail the check if the burned proportion of its catchment is less than this value.

Example:

# Require the catchment to be at least 25% burned
min_burn_ratio = 0.25

CLI option: --min-burn-ratio

Python kwarg: min_burn_ratio

max_developed_area_km2
Type:
float
Default:
0.025

The maximum amount of developed catchment area in square kilomters. Used to check if a segment is sufficiently undeveloped. A segment will fail the check if the amount of developed catchment is greater than this value.

Example:

# Segments cannot have more the 0.025 km2 of development
max_developed_area_km2 = 0.025

CLI option: --max-developed-area-km2

Python kwarg: max_developed_area_km2

max_confinement
Type:
float
Default:
174

The maximum confinement angle in degrees. Used to check if a segment is sufficiently confined. A segment will fail the check if its confinement angle is greater than this value.

Example:

# Do not allow confinement angles greater than 174 degrees
max_confinement = 174

CLI option: --max-confinement

Python kwarg: max_confinement

confinement_neighborhood
Type:
int
Default:
4

The pixel radius used to compute confinement angles.

Example:

# Use a 4-pixel radius to compute confinement angles
confinement_neighborhood = 4

CLI option: --neighborhood

Python kwarg: confinement_neighborhood

flow_continuous
Type:
bool
Default:
True

Whether to preserve flow continuity in the network. If True, segments whose removal would break flow continuity will be retained in the network, even if they fail the filters. If False, all segments that fail the filters will be removed.

Example:

# Do not preserve flow continuity
flow_continuous = False

CLI option: --not-continuous

Python kwarg: flow_continuous

Remove IDs

remove_ids
Type:
[int, ...]
Default:
[]

The segment IDs of segments that should be removed from the network after filtering. Useful when the network contains a small number of problem segments. You can obtain Segment IDs by examining the Segment_ID field in the assessment results. Segment IDs are constant after delineation, but can change if you alter delineation settings.

Example:

# Remove segments 7, 19, and 22
remove_ids = [7, 19, 22]

CLI option: --remove-ids

Python kwarg: remove_ids

Hazard Modeling

Numeric parameters used to run the hazard assessment models.

I15_mm_hr
Type:
[float, ...]
Default:
[16, 20, 24, 40]

Peak 15-minute rainfall intensities in millimeters per hour. Used to compute debris-flow likelihoods and volumes, which are used to classify combined hazards.

Example:

# Estimate likelihood, volumes and hazards
# for I15 of 16, 20, 24, and 40 mm/hour
I15_mm_hr = [16, 20, 24, 40]

CLI option: --I15-mm-hr

Python kwarg: I15_mm_hr

volume_CI
Type:
[float, ...]
Default:
[0.95]

The confidence intervals to calculate for the volume estimates (from 0 to 1).

Example:

# Compute 90% and 95% confidence intervals
volume_CI = [0.9, 0.95]

CLI option: --volume-CI

Python kwarg: volume_CI

durations
Type:
[float, ...]
Default:
[15, 30, 60]

The rainfall durations (in minutes) that should be used to estimate rainfall thresholds. Only values of 15, 30, and 60 are supported.

Example:

# Compute thresholds for all 3 rainfall durations
durations = [15, 30, 60]

CLI option: --durations

Python kwarg: durations

probabilities
Type:
[float, ...]
Default:
[0.5, 0.75]

The debris-flow probability levels used to estimate rainfall thresholds (from 0 to 1).

Example:

# Compute thresholds for 50% and 75% probability levels
probabilities = [0.5, 0.75]

CLI option: --probabilities

Python kwarg: probabilities

Basins

Options for locating outlet basins.

locate_basins
Type:
bool
Default:
True

Whether to locate outlet basins. Setting this to False can significantly speed up runtime, but the assessment results will not include values for the basins.

Example:

# Do not locate outlet basins
locate_basins = False

CLI option: --no-basins

Python kwarg: locate_basins

parallelize_basins
Type:
bool
Default:
False

Whether to use multiple CPUs to locate outlet basins. Using this option creates restrictions for running wildcat within Python. See the following for details: Parallelizing Basins

Example:

# Use multiple CPUs to locate basins
parallelize_basins = True

CLI option: --parallel

Python kwarg: parallelize_basins