E3S Web of Conferences 415, 05029 (2023)
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https://doi.org/10.1051/e3sconf/202341505029

Advancing debris flow hazard and risk assessments using
debris flow modeling and radar derived rainfall intensity data
Thad Wasklewicz1*, Richard Guthrie2, Graham Knibbs3, and Rebecca Rossi4
1Stantec, Geohazards and Geomorphology, Fort Collins CO, USA
2 Stantec, Geohazards and Geomorphology, Calgary AB, Canada
3 Stantec, Geohazards and Geomorphology, Burnaby BC, Canada
4 Stantec, Geohazards and Geomorphology, Sacramento, CA, USA

Abstract. Debris flow hazard and risk assessments are critical tools in mitigating and planning forthese
events. Existing debris flow hazard assessments can provide a rapid view of the likelihood of debris flows
in recently burned watersheds and along stream segments within the watershed. Furthermore, debris flow
volumes can be predicted for these watersheds and along the stream segments. Advances in modeling andremote-sensing data can add further value to the rapid assessments. Here, modeled debris flow volumes anda more detailed understanding of rainfall conditions highlight a need to reconcile debris flow probabilities
and volumes using local conditions. Modeled debris flow volumes are consistently lower than even thelowest predicted volumes from empirical models used in the debris flow hazard assessments. Watershedprobability and volume relations also over predict based on our probabilities derived from rainfall intensityfrom Multi-Radar/Multi-Sensor System 1-hr data. Probability and volume measures need to be further
considered as the conservative measures of the rapid assessments have implicationfor risk analyses requiredfor planning and management decisions, and ultimately for design and cost of mitigation to manage risk.

1 Introduction
Prediction of post-wildfire debris flows has been a
constant theme of geomorphology and geohazard
research. Climate change has been linked to increased
magnitude and frequency of wildfires in many parts of
the world [1] and an expected increase of extreme
rainfall following wildfires [2], which have further
complicated this understanding as well as heightened
the need for scientists and engineers to rapidly advanceour understanding of post-wildfire debris flow initiation,runout, and inundation.
Knowledge of debris flow initiation has grownincrementally through investigations of rainfall
intensity-duration thresholds [3, 4], landslide-generated
debris flows [5, 6], progressive sediment-bulking [7],and sediment availability [8] among others. Whilesignificant advances have been brought about by these,and other studies, there is a need for further
developments that inform models and allow us to predictthe likelihood of debris flow occurrence, runout, andvolumes to better assess the potential for debris flow
hazards and risks.
Here, a combination of rainfall intensity thresholds
and debris flow modeling provide new insights into thelikelihood of debris flows, as well as begin to advanceour understanding of debris flow volume estimates.
These findings help advance analyses beyond thewatershed approaches commonly used for debris flow
hazard assessment and volume measurement tools usedin the United States. The findings provide further
information to inform debris flow hazard and risk
analyses and aid communities, government agencies,
and industry recognize and mitigate these hazards.
1.1
Current US debris flow hazard
assessment
The most utilized emergency (rapid) assessment of
post- fire debris flow hazards in the United States ofAmericacontinues to be the model (referred to USGSModel) developed by the United States Geological
Survey Landslide Hazards program [4, 9]. The
likelihood model produces probability and volume ofdebris flow data forrecently burned areas using inputs
from basin shape and size, topography, soil burnseverity, soil properties, and 15-minute rainfall
intensity. The debris flow likelihood model is based ona logistic regression approach to predict the probabilityof debris flows at the watershed and stream segmentlevel as a percentage of the likelihood [4]. Volumecalculations are derived from multiple linear
regression models that estimate the volume of material
at point within the watershed, as wellas at the outlet ofthe watershed [10]. The approach has been usedbroadly across Western United States but has largely
been based on research and findings developed fromSouthern California.

* Corresponding author: Thad.Waskelwicz@stantec.com© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 05029 (2023)
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1.2

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Current knowledge on debris flows

Interstate-70 were linked to watersheds.

There have been strong links made to rainfall thresholds
and shallow landslide and debris flow formation in avariety of locations [11]. This work was grounded inlandslide triggering thresholds from Caine 1980 [12].
Initial empirically derived post-wildfire rainfall ID
thresholds were developed for Southern California byCannon et al. [13] and expanded upon by Staley et al.
[3]. The <60-minute rainfall intensities were identified
as important from this initial work. The 15-min durationthresholds were later found to lead to the most accurate
predictions of the post-wildfire debris flow initiation in
the USGS logistic regression equations and fieldmeasurements [14], which are well identified forSouthern California and other locations in the WesternUS. Recent research has identified the potential forvariability in the rainfall ID thresholds based on climate,geology, and hydraulic properties of burned soils [15],which has implications for refining this type ofmodeling approach.
Predictive models of post-wildfire debris flow
occurrence, magnitude, and volumes have been usedfrequently. The model used in the USGS debris flow
hazard assessment is an empirical model developedfrom
data in Southern California [10]. This model relies on
sediment volumes from debris flows and debris floods
derived from debris-retention basins at watershedoutlets.
New models have extended analyses to estimate theamount of post-wildfire debris flow sedimenttransported throughout watershed [16] and to alluvial
fans [17]. Other recent modeling experiments haveidentified topography, burn severity, and the percent soilorganic matter following wildfire to be more important
than rainfall when predicting debris flow volumes [18].
Sediment sources are being more widely recognized as
key contributors to debris flow development andpropagation [8].

2Fig. 1. Subject wildfire locations in Colorado, USA.

A total of 329 initiation points identified from Goggle
Earth Imagery were linked to the nearest watershed thatexperienced adebris flow. A landslide mask that aligned
to the griddedprecipitation data was developed and used
to estimate the time of landslide occurrence at amaximum 1-hr rainfall intensity based on the nearest
known debris flowat the outlet of a watershed. Rainfall
was grouped into 1mm/hr intensity bins and the numberof landslides perbin were used to estimate a probability(Fig. 2).

Study areas

Fig. 2. Probability of landslides per hectare fromGrizzlyCreek Fire.

The focus is on three wildfires, Cameron Peak, East
Troublesome, and the Grizzly Creek Fires, occurring in2020 - an active wildfire season in Colorado (Fig.1).
The Cameron Peak Fire was reported on August 13,2020, and burned an area of 84,544 ha over 112 days.
The East Troublesome Fire was reported on October 14,2020, and consumed 78,437 ha over 47 days. The
Grizzly Peak Fire started on August 10, 2020, andconsumed 13,205 ha over 130 days.

3The 15-min rainfall intensity was extracted nearthehead of multiple watersheds in each of the wildfires
to develop an understanding of the return interval forthe peak 15-minute intensity of 24 mm/h used in theUSGS debris flow assessment.
Debris flow modeling was performed using
DebrisFlow Predictor [19], which is a probabilistic
cellular automata model. Models were locallycalibrated using a variety of parameters at each of thefires.
Debris flow volumes generated from the USGS
Debris Flow Hazard Assessment, and from our model
were compared for the Cameron Peak, Grizzly Creek,
and East Troublesome study areas across USGS
defined watersheds.
Modeled debris flow outputs were compared to thecorresponding minimum USGS debris flow volumes
across all drainage sizes and to typical USGS debris
flow volumes at four subbasins. For each USGS basinthroughout the study areas, the total modeled volume
was calculated as the sum of all debris flow volumesMethods

We mapped the post-wildfire landslides from several
storms of varying intensity the year after the Grizzly
Creek Fire. One-hour rainfall intensity data from theMulti-Radar/Multi-Sensor System (MRMS) werecollected to aid in the development of a rainfall intensityand probability/ha/duration relationship for the Grizzly
Peak Fire. MRMS data were clipped to the Grizzly
Creek Fire perimeter and interpolated to hectare-sized
grids. Known debris flow locations from outlets along2

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along the runout paths that initiated from thecorresponding USGS basin.

4

estimated would require an unusually large percentageof the basins to be subject to landslides.
Table 1. USGS debris flow probability and volume atwatershed-scale.
USGS
Basin
Basin
Basin
VolumeAreaFire
LandslideName(m3)
2(km )
Probability
GrizzlyBlue
0.684
4.632206
CkGulchGrizzlyUnnamed
0.6
1.816510
CkCameron
Unnamed
0.677
0.116269
Pk
CameronSheep
0.623
7.532206
Pk
Gulch

Results

Across all drainage basin sizes, the modeled debris
flowvolume is like, but less than, the minimum volume
calculated by the USGS (Fig.3). The contrast between
the modeled volumes versus USGS minimum debris
flow volume is consistent regardless of drainage basin
size or differences between wildfires.

To refine the estimates of debris flow activity, we
use the relation from Figure 2 to adjust for the areabased probability of severely burned watersheds (Table2). The likelihood of at least one landslide is higher forlarger watersheds and probabilities for more than one
landslide decline rapidly for smaller basins (Table 2).
Typical modeled volumes for an individual landslide
within the study areas is small, while interactions
between landslides leads to larger expected debris flow
volumes. However, these volumes do not reach themagnitude of the medium volume calculations from
USGS assessments (Table 1). It is the authors’ belief
that this latter approach provides better approximationof hazards by accounting for burned ground exposed tohigh intensity rainfall.

Fig. 3. Comparison of USGS volumes and modeled debris
flow volumes across drainage basin size at Cameron Peakstudy area.

To better understand the difference between ourmodel and the USGS model, and perhaps refine the postwildfire hazard assessment, results from four typical
sub-basins with high burn severity were examined(Table 1). Probability is largely driven by burn severity
irrespective of basin size. Further, the volumes

Table 2. Probabilities for the number of potential debris flows from recently burned watershed.

FireBasin
Name

USGS Basin
Landslide
ProbabilityBasin
Area(km2)
12

34

56

7

Grizzly CkBlue Gulch

0.684

4.6

0.953

0.804

0.574

0.342

0.171

0.073

0.027

Grizzly Ck
CameronPk
CameronPk

Unnamed

0.6

1.8

0.697

0.323

0.105

0.025

0.004

0-0.0006

Unnamed

0.677

0.1

0.066

0.002

0-4E-05-4E-05-4E-05-4E-05

Sheep
Gulch

0.623

7.5

0.993

0.958

0.868

0.718

0.532

0.351

0.206

18373674

5511

7348

9185

11022

12859

Typical modeled landslide size in m3

5

Probability of at least n landslides

infrastructure. However, the cost-benefit ratio ofdesigning to this level might be cost prohibitive. We
acknowledge further modeling and research is required
to understand why these volumes differ and what are
best design criteria would be once these volumes are
more thoroughly reconciled. Our experience indicatesthat the approaches presented here permit us to model
debris flows using a more nuanced view of debris flow
initiation likelihoods in the watersheds which improves
our capabilities to assess risk to infrastructure andproperties. Further refinement and validation of theseapproaches will enhance our ability to identify thepotential for debris flow initiation, assess runout andConclusions

Our initial findings show a disparity in the debris flow
volumes developed by the different approaches. These
differences have direct implications for designingmitigation prior to and after wildfires. The larger
volumes derived from the USGS empirical equations
would provide a more conservative measure requiring
more substantial structures to address the loads, impacts,runups and volumes of material released in the event ofdebris flow. Building this structure(s) would also
potentially reduce the risk to communities and3

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inundation before and after wildfires, and provide amore detailed understanding of the risk to property andlives.

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