E3S Web of Conferences 415, 05016 (2023)
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https://doi.org/10.1051/e3sconf/202341505016

Integration of meteorology and geomorphology for enhanced
understanding of post-fire debris-flow hazards
Nina Oakley1,2*, Luke McGuire3, and Jeremy Lancaster4
1

California Geological Survey, Burned Watershed Geohazards Program, Sacramento, California, USA
University of California, San Diego, Center for Western Weather and Water Extremes, La Jolla, California, USA
3
University of Arizona, Geoscience Department, Tucson, Arizona, USA
4
California Geological Survey, Geologic and Landslide Mapping Program, Sacramento, California, USA
2Abstract. Through precipitation, the fields of meteorology and geomorphology are fundamentally linked,
thus interdisciplinary efforts are needed to advance understanding and warning of rainfall-driven
geohazards. With a focus on recent efforts specific to post-fire debris flows in California, our presentation
provides an overview of the benefits and challenges of working in an interdisciplinary team ofmeteorologists and geomorphologists, as well as results of a recent project demonstrating advancement
through the integration of these fields. In this project, we combine high-resolution ensemble precipitation
forecasts with post-fire debris-flow models to explore the feasibility and potential value of providing
probabilistic post-fire debris-flow hazard information over a burn scar. In sharing these examples, weemphasize the multi-benefit nature of these efforts, and encourage future interdisciplinary efforts that
improve warning and mitigation of rainfall-driven geohazards.

1 Introduction
California experiences frequent, damaging post-wildfire
debris flows [1-2]. Post-wildfire debris flows are
initiated by runoff during short-duration, high-intensity
rainfall, with rainfall over a 15-minute duration being agood predictor [3-4]. Historically, post-fire debris-flow
research broadly describes meteorological conditions
triggering debris flows as “convective storms” or“frontal storms” [1]. However, from a meteorologicalperspective, there are a range of atmospheric conditions
within these broad categories that affect the intensity,duration, and spatial extent of precipitation over a burnscar, which influences geomorphic response. These
atmospheric processes relevant to debris-flow hazardsgenerally operate on a spatial scale of ~2-200 km, and atemporal scale of <24h [5], referred to as the“mesoscale”. This is in contrast to the larger “synopticscale”, featuring cyclones and atmospheric rivers, andsmaller microscale processes, though interactions acrossscales can influence weather outcomes.
Understanding the mesoscale characteristics ofstorms that produce post-fire debris flows is critical tointerpreting past events and being able to skillfully
predict outcomes in future events, including theprovision of early warning to communities at-risk. In apaper describing priority research needs on post-firerunoff, Moody et al. [6] state that, “collaboration
between post-wildfire researchers and mesoscalemeteorologists will be needed to advance themeasurement, understanding and prediction of temporaland spatial variations in rainfall over burn areas.”
Herein, we share recent interdisciplinary Californiafocused
work
among
meteorologistsandgeomorphologists(encompassing
geologists,hydrologists, civil engineers, and other nonmeteorologists working on the post-fire debris-flow
topic) to advance understanding and early warning of
post-fire debris flows. We note the benefits of suchcollaborations, challenges and suggestions to overcome
them, as well as present the results of recent
interdisciplinary research that combines mesoscaleatmospheric modeling with post-fire debris-flow
likelihood, volume, and inundation modeling to provide
probabilistic hazard information as an example ofadvancements through interdisciplinary work.

2 Interdisciplinary efforts between
meteorologists and geomorphologists
There are numerous ways geomorphologists andmeteorologists can work collaboratively to improveunderstanding of post-fire hydrologic hazards. One suchapproach is using post-fire debris-flow observations
such as event timing, magnitude, observed rainfall, andexamining the atmospheric conditions associated withthe triggering rainfall. This can help contextualize thestorm characteristics as a rare or common event, giving
insight beyond the rainfall annual recurrence interval.

* Corresponding author: nina.oakley@conservation.ca.gov© 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/).

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As a recent example, Figure 1 shows radar reflectivity
associated with a post-fire debris flow on the Bond Firein southern California. This is a feature known as a“narrow cold frontal rainband” (or NCFR), a narrow
band of high-intensity rainfall along a storm’s cold
front. Such features are relatively common in this area;
southern California experiences approximately threesuch events per year [7].

been available at spatial and temporal scales too coarse
for effective use. While this is due in part to thecomputational resources required to produce highresolution information, it has also been due to a lack ofdemand for high-resolution products. A strong
relationship between the two disciplines provides apathway for requests to reach weather and climate
modelers, and in turn provides justification for thesemodelers to develop high-resolution products. With
recent efforts to communicate these needs, more highresolution products are emerging [8].
Though there are numerous benefits, conducting
interdisciplinary work is not without challenges.
Members of an interdisciplinary science team likely use
different terminology, norms, research methods, andmay have different goals. Tasks likely requireinterdependence across researchers from different
disciplines, which can at times make it challenging toplan a workflow [9], thus progress may not be as linear
as when working in a single discipline. From theauthors’ experience, developing a new interdisciplinary
collaboration requires patience, a willingness to ask
questions about the field that is not your own, and tolisten and learn. There can be presumptions researchers
have about their knowledge of the other field that createa barrier for communication of scientific information. A
first step towards success is to find research teammembers who have openness to interdisciplinary work[9]; not all researchers are well-suited to thesecollaborations. Some scientists lack interest outside
their field or do not give sufficient consideration thenuances that are important to other fields (e.g.,
meteorologists may have limited interest in the range ofpost-fire hydrologic responses). Overcoming thesechallenges may require persistence to find the right
teammates as well as time to build trust andunderstanding among the interdisciplinary teammembers.

Fig. 1. Radar reflectivity on 14 December 2021 at 18:45 UTC
showing a narrow cold frontal rainband impacting the 2020
Bond Fire burn scar, outlined in red (top); image from
California-Nevada River Forecast Center. A home destroyedin the debris flow associated with this rainfall event (bottom);
image from California Geological Survey.

Conducting such analyses over numerous eventsand locations reveals regional patterns of atmosphericfeatures and conditions associated with debris-flow
events [2], as well as an understanding of the types ofatmospheric events that are most likely to pose thegreatest potential for impactful events. In the US, theNational Weather Service is the entity responsible for
issuing watches and warnings for post-fire debris flows
to the public. Having information on atmosphericconditions associated with past impactful debris flows
contributes to forecaster situational awareness andconfidence in issuance of watches and warnings.

3 Interdisciplinary
collaboration
example: Forcing debris-flow models
with ensemble precipitation forecasts
The National Weather Service is moving towards
probabilistic hazard information and impact-baseddecision support [10]. Thus, there is a need to develop
methods to integrate atmospheric models and debris
flow models in a way that they can be used operationallyand provide probabilistic and impact-based information
on post-fire debris-flow hazards.

Working collaboratively allows for novel use of
atmospheric datasets or methods that may not have beenpreviously known to the geomorphology community.
This may include rain gauge data, gridded precipitation
estimates, forecast tools or resources, and other weather
information. These interdisciplinary interactions also
lead to the exploration of science questions andsituational awareness needs, facilitating thedevelopment of new precipitation or weather productsthat support post-fire debris-flow research. Historically,tools needed to evaluate the precipitation characteristics
of past debris-flow events (e.g., gridded quantitative
precipitation estimates) and future potential for debrisflow hazards (climate model projections) have only

To address this need, we experiment with using
probabilistic precipitation forecast information as an
input to debris-flow hazard models. We utilize a 100member ensemble forecast from a numerical weathermodel at 1-km horizontal resolution with a 24-hour leadtime for two storm events featuring narrow cold frontal
rainbands. We focus on the Thomas Fire burn area insouthern California, as major debris flows initiatingfrom within the burn scar on 9 January 2018 resulted in
23 deaths and nearly $1 billion in damages [11-12]. Both2

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storms selected for the forecast simulation, 9 January
2018 and 2 February 2019, resulted in impacts on theThomas Fire burn scar. Though we ran the forecast
retrospectively, the inputs selected to initiate theforecast model run retain the uncertainty of the forecast
as if it were run 24 hours prior to the event.

Fig. 2. Likelihood and volume analysis for the Thomas Fireburn area for the 9 January 2018 storm event. Left panels show
the Weather Research and Forecasting model (WRF) rainfall
forecast ensemble mean and right panels show the standard
deviation for each watershed for peak I15 rainfall (a,b);
probability of debris-flow occurrence (c,d); debris-flow
volume estimate (e,f). Watersheds outlined in red in (a)experienced notable impacts. Figure developed by Tao Liu.

The storm maximum 15-minute intensity wascalculated for each of the 100 forecast ensemble
members for each storm and used as an input to models
that predict post-fire debris-flow likelihood [4] andvolume [13] at the watershed scale. From there, we cancalculate statistics for debris-flow likelihood andvolume such as the ensemble mean and standard
deviation (Fig. 2) or number of ensemble members withlikelihood or volume over a specified threshold. Lastly,
we used the outputs of the likelihood and volume models
as inputs into a debris-flow inundation model [14] togenerate maps of debris-flow inundation extent.
Propagating data from the precipitation forecast
ensemble into the inundation model, which requires
debris-flow volume (a function of I15) as an input,
provides information on the potential range of area
inundated with the incoming storm (Fig. 3), as well as
the ability to calculate likelihood of inundation for alocation in a forecast storm.

Fig. 3. Example of inundation outcomes for several
watersheds within the Thomas Fire burn area using
precipitation input from two forecast ensemble members withdifferent maximum 15-minute rainfall intensities (I15) for the9 January 2018 storm that impacted the area. Use of theensemble allows for display of a range of possible outcomes.

4 Final Remarks
This work provides a proof-of-concept for
probabilistic debris-flow hazard information based offof ensemble precipitation forecasts, though there areseveral limitations to consider in moving thisexperiment towards operations. Running large ensemble
weather forecasts at high resolution is very
computationally expensive. Additional research needs
to be done to optimize the model setup such that itprovides sufficient uncertainty quantification whilerunning fast enough to be used operationally. Mesoscalemodels can represent features producing short-duration,
high-intensity rainfall, but can struggle with exact
location, timing, and intensity of these features out pasta few hours of forecast lead time. The use of anensemble helps overcome this issue. Debris-flow
likelihood, volume, and inundation models are subject
to large uncertainty and may not perform well outside oflocations for which there has been sufficient datacollected to calibrate them.

Probabilistic hazard information such as that
presented in this experiment quantifies uncertainty inboth the precipitation forecast and potential debris-flow
response, providing enhanced information over adeterministic (singular) precipitation forecast or
application of a uniform rainfall intensity over a burnarea. This approach supports decision-makers in
identifying areas most likely to have impactful debris
flows and provides them a sense of the potential rangeof outcomes associated with the incoming storm, which
can help determine how to manage storm response
resources. This is especially valuable given the growing
size and frequency of wildfires in California.

Additionally, this work demonstrates progress
towards the development of an operational tool thatprovides probabilistic hazard information for post-fire3

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debris flows. Such advancement will be a major step
forward in situational awareness and warning of thesehazards. These advances would not be possible without
close collaboration between the geomorphology andmeteorology communities. While we focus on post-firedebris flows here, interdisciplinary collaborations inthese two fields have also led to improved understandingof other rainfall-driven geohazards, such as shallow
landslides [15]. These collaborations are beneficial, if
not necessary, to advance early warning systems andimprove tools that support risk management. We
encourage geomorphology and meteorology colleagues
to seek out collaborations to facilitate progress in bothfields that supports improved warning and mitigation ofrainfall-driven geohazards.

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