Prepared in cooperation with the Bernalillo County Natural Resources Services

Potential Postwildfire Debris-Flow Hazards—A Prewildfire 

Evaluation for the Sandia and Manzano Mountains and 

Surrounding Areas, Central New Mexico

Scientific Investigations Report 2014–5161

U.S. Department of the Interior

U.S. Geological Survey
Cover photograph. View of Sandia Mountains looking south from Sandia Crest. Photograph by Peter Voshefski.
Potential Postwildfire Debris-Flow 

Hazards—A Prewildfire Evaluation for 

the Sandia and Manzano Mountains and 

Surrounding Areas, Central New Mexico

By Anne C. Tillery, Jessica R. Haas, Lara W. Miller, Joe H. Scott, and 

Matthew P. Thompson

Prepared in cooperation with the Bernalillo County Natural Resources Services

Scientific Investigations Report 2014–5161

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Tillery, A.C., Haas, J.R., Miller, L.W., Scott, J.H., and Thompson, M.P., 2014, Potential postwildfire debris-flow hazards—A prewildfire evaluation for the Sandia and Manzano Mountains and surrounding areas, Central New Mexico: 

U.S. Geological Survey Scientific Investigations Report 2014–5161, 24 p. with appendix, http://dx.doi.org/10.3133/

sir20145161.

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-3857-9
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................2

Description of Study Area ...................................................................................................................3

Methods and Approach................................................................................................................................3

Modeling Extent ....................................................................................................................................5

Modeling Results ...........................................................................................................................................6

FlamMap Fire Behavior Simulation Results .....................................................................................6

Burn Probability Modeling Results ....................................................................................................6

Debris-Flow Probability and Volume Estimates...............................................................................6

Hazard Assessment.......................................................................................................................................8

Ortiz Mountains Area ...........................................................................................................................8

Sandia Mountains Area.......................................................................................................................9

Manzanitas and Northern Manzano Mountain Area......................................................................9

East Mountain Area..............................................................................................................................9

Manzano Mountain Area.....................................................................................................................9

Los Pinos Mountain and Chupadera Area......................................................................................10

Integrated Relative Debris-Flow Hazard Rankings ................................................................................10

Limitations of Hazard Assessment............................................................................................................11

Future Considerations for Prewildfire Assessments of Postwildfire Hazards ..................................12

Summary........................................................................................................................................................12

Acknowledgments.......................................................................................................................................13

References Cited..........................................................................................................................................13

Appendix........................................................................................................................................................15
iv

Figures

1. Map showing location of study area.........................................................................................4

2. Flow chart showing model interactions....................................................................................5

3. Maps showing ignition density grid based on 1992–2011 fire occurrence dataset

and historic fire occurrence in large fire simulation system modeling extent...................5

4. Map showing FlamMap simulation results ..............................................................................7

5. Map showing results of burn probability simulation using large fire simulation 

system.............................................................................................................................................6

6. Maps showing debris-flow probability and debris-flow volume..........................................6

7. Graph showing comparison of debris-flow probability subbasin totals for 5- and

10-year recurrence interval, 30-minute rainfall events..........................................................8

8. Maps showing percentile of continuously parameterized burn probability indices

and integrated debris-flow hazard indices plate ..................................................................10

9. Scatterplot of conditional debris-flow volume with debris-flow likelihood index for

all modeled subbasins ...............................................................................................................11

Table

1. Summary of debris-flow probability values by subbasin in response to 5- and

10-year recurrence interval, 30-minute rainfall events..........................................................8

Appendix Figures

1–1. Flow chart showing model interactions..................................................................................16

1–2. Maps showing fuel models.......................................................................................................20

1–3. Maps showing comparison of unadjusted burn probability to basin-average burn

probability indices ......................................................................................................................22

Appendix Table

1–1. Weather and fuel moisture information at Oakflats Remote Automated Weather

(RAW) station as reported by Fire Family Plus and modified as noted..............................19
v

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

square hectometer (hm2) 2.471 acre

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 0.0002642 million gallons (Mgal) 

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 1.308 cubic yard (yd3) 

cubic meter (m3) 0.0008107 acre-foot (acre-ft) 

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88)

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83)

Potential Postwildfire Debris-Flow Hazards—A 

Prewildfire Evaluation for the Sandia and Manzano 

Mountains and Surrounding Areas, Central New Mexico

By Anne C. Tillery1, Jessica R. Haas2, Lara W. Miller3, Joe H. Scott4, and Matthew P. Thompson2

1

U.S. Geological Survey.

2

U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station.

3

The Nature Conservancy, Santa Fe, New Mexico.

4

Pyrologix LLC.

Abstract

Wildfire can drastically increase the probability of 

debris flows, a potentially hazardous and destructive form of 

mass wasting, in landscapes that have otherwise been stable 

throughout recent history. Although there is no way to know 

the exact location, extent, and severity of wildfire, or the 

subsequent rainfall intensity and duration before it happens, 

probabilities of fire and debris-flow occurrence for different 

locations can be estimated with geospatial analysis and modeling efforts. The purpose of this report is to provide information 

on which watersheds might constitute the most serious, potential, debris-flow hazards in the event of a large-scale wildfire 

and subsequent rainfall in the Sandia and Manzano Mountains. 

Potential probabilities and estimated volumes of postwildfire 

debris flows in the unburned Sandia and Manzano Mountains 

and surrounding areas were estimated using empirical debrisflow models developed by the U.S. Geological Survey in 

combination with fire behavior and burn probability models 

developed by the U.S. Department of Agriculture Forest 

Service.

The locations of the greatest debris-flow hazards correlate with the areas of steepest slopes and simulated crownfire behavior. The four subbasins with the highest computed 

debris-flow probabilities (greater than 98 percent) were all 

in the Manzano Mountains, two flowing east and two flowing west. Volumes in sixteen subbasins were greater than 

50,000 square meters and most of these were in the central 

Manzanos and the western facing slopes of the Sandias.

Five subbasins on the west-facing slopes of the Sandia 

Mountains, four of which have downstream reaches that 

lead into the outskirts of the City of Albuquerque, are among 

subbasins in the 98th percentile of integrated relative debrisflow hazard rankings. The bulk of the remaining subbasins in 

the 98th percentile of integrated relative debris-flow hazard 

rankings are located along the highest and steepest slopes 

of the Manzano Mountains. One of the subbasins is several 

miles upstream from the community of Tajique and another is 

several miles upstream from the community of Manzano, both 

on the eastern slopes of the Manzano Mountains.

This prewildfire assessment approach is valuable to 

resource managers because the analysis of the debris-flow 

threat is made before a wildfire occurs, which facilitates 

prewildfire management, planning, and mitigation. In northern 

New Mexico, widespread watershed restoration efforts are 

being carried out to safeguard vital watersheds against the 

threat of catastrophic wildfire. This study was initiated to help 

select ideal locations for the restoration efforts that could have 

the best return on investment.

Introduction

Wildfire is a natural process in forest ecosystems, and 

occurs with varying frequencies and severities depending on 

landscape characteristics, climatic conditions, and the historical fire regime. Although attention often is focused on the 

potential damages from wildfire in the wildland-urban interface, wildfire also presents a threat to critical infrastructure 

including flood water conveyances and water conveyances 

critical to municipal water supplies. Further, burned landscapes are at risk of damage from postwildfire erosion, such as 

that caused by debris flows and flash floods, which can be the 

most catastrophic of the postwildfire threats to an area.

Debris flows are high-density slurries of water, rock fragments, soil, woody debris, and mud that can have enormous 

destructive power particularly when they are fast moving. 

Debris flows are a common geomorphic process in response 

to intense rainfall in some unburned watersheds that have 

steep slopes, ample erodible materials, and minimal infiltration 

(Elliott and others, 2012). Wildfire can drastically increase the 

probability of debris flows in landscapes that have otherwise 

been stable throughout recent history. A primary watershed 

effect of wildfire is rapid and dramatic decrease in infiltration 
2 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

because of widespread removal of vegetation and development 

of hydrophobic soils (Cannon and Gartner, 2005). Although 

there is no way to know the location, extent, and severity 

of wildfire, or the subsequent rainfall intensity and duration 

before it happens, probabilities of fire and debris-flow occurrence for different locations can be estimated with geospatial 

analysis and modeling efforts. These models can be useful 

planning tools for better understanding and mitigating the risks 

of potential postwildfire debris flows.

Debris flows have been documented after many fires in 

the western United States (Cannon and others, 2001a and b; 

Cannon and others, 2010; DeGraff and others, 2011; Kean 

and others, 2011). In addition, debris flows following wildfire 

can be generated in response to low-recurrence interval/high 

intensity rainfall. Recently burned landscapes may be at risk of 

such postwildfire hydrologic hazards for several to many years 

following the fire (Cannon and Gartner, 2005). The U.S. Geological Survey (USGS) has developed a model (Cannon and 

others, 2010) to estimate postwildfire debris-flow probability and volume. This information can be used to determine 

watersheds of concern or areas most at risk for loss of life and 

property.

A second, key spatial variable for risk assessment and 

prioritization efforts is wildfire likelihood (Scott and others, 

2013), typically measured as annual burn probability. Numerous studies have linked wildfire occurrence and extent to 

increasing spring and summer temperatures (Westerling and 

others, 2006; Swetnam and Betancourt, 1990; Balling and 

others, 1992; Pierce and others, 2004). The warmest and driest 

2-year period since record-keeping began in New Mexico in 

the late 1800s was during 2011–12 (Charles H. Jones, National 

Weather Service, written commun., 2014) and the wildfire 

seasons during those years included two of the largest fires 

in the State’s history. The seasonal drought outlook (National 

Weather Service, 2014) indicates that drought is likely to 

persist or intensify throughout the southwestern states at least 

through June 2014. The threat of severe wildfires is likely to 

persist in New Mexico with continuing hot and dry weather 

patterns, hence the need for prewildfire assessment and mitigation efforts.

Localized variation in the probability of burning is 

affected by factors such as topography and fuel, or vegetation 

characteristics, as well as fire weather and ignition patterns. 

Spatial information on wildfire probability makes it possible 

to distinguish across basins and subbasins with potentially 

different likelihoods of experiencing wildfire, which can be an 

important distinction for efficient prioritization of mitigation 

efforts. Information on wildfire probability is therefore critical 

for prewildfire risk assessment. Combining the debris-flow 

models with models for fire behavior and burn probability 

developed by the U.S. Department of Agriculture Forest Service (USFS) (Finney, 2006; Finney and others, 2011) allows 

for characterization of potential threats of postwildfire debris 

flows in watersheds that have not experienced wildfires in 

recent years.

A prewildfire evaluation to determine potential for postwildfire debris flows in the Sandia Mountains in central New 

Mexico was started in 2013 by the USGS in cooperation with 

Bernalillo County Natural Resources Services as a part of the 

Rio Grande Water Fund. The Manzano Mountains were later 

added to the study area for this evaluation because of their 

proximity to the Sandia Mountains and the ease in expanding the study area to include them. The USFS and The Nature 

Conservancy provided support for this effort, principally 

through fire simulation modeling.

The Rio Grande Water Fund is a groundbreaking project 

that engages private and public partners in protecting vital 

watersheds in northern New Mexico with a primary goal to 

generate sustainable funding for a 10–30-year program of 

large-scale forest and watershed restoration treatments, including thinning overgrown forests, restoring streams, and rehabilitating areas that experience flooding and other damaging 

effects after wildfires (The Nature Conservancy, 2014). This 

study was initiated to provide information on which subbasins 

might constitute the most serious, potential debris-flow hazards 

in the event of a large-scale wildfire and subsequent rainfall in 

the Sandia and Manzano Mountains and surrounding areas.

Purpose and Scope

The purpose of this report is to present estimates for 

the likelihood and potential magnitude of postwildfire debris 

flows for the unburned areas of the Sandia and Manzano 

Mountains and surrounding areas. The study area includes all 

mountainous regions in the Sandia and Manzano Mountains 

and extends to the break in slope at the base of the mountains 

on all sides. The overarching modeling effort involved the 

coupling of multiple models for estimating spatial variation in 

burn probability, burn severity, and debris-flow hazard and is 

described in detail in appendix 1. The USFS large-fire simulation system referred to as FSim (Finney and others, 2011) 

was used to estimate burn probability and the USFS firebehavior model FlamMap (Finney, 2006) was used to estimate 

crown-fire activity and to infer burn severity likely to occur 

in the study area. The USGS postwildfire debris-flow models 

(Cannon and others, 2010) were used to make estimates of the 

probabilities of debris flows and of volumes of material that 

could be transported through subbasins based on topography, 

soil characteristics, and simulated burn intensities.

This prewildfire assessment approach is valuable to 

resource managers because the analysis of the debris-flow 

threat is made before a wildfire occurs, which facilitates 

prewildfire management, planning, and mitigation. Widespread watershed restoration efforts are being undertaken to 

safeguard vital watersheds against the threat of catastrophic 

wildfire in northern New Mexico (The Nature Conservancy, 

2014). This study was initiated to help select ideal locations 

for the restoration efforts that are the most appropriate and 

provide the best benefits.
Methods and Approach 3

Description of Study Area

The study area includes the Sandia and Manzano Mountains and surrounding areas east and south of the City of 

Albuquerque, in central New Mexico (fig. 1). The Sandia 

Mountains (hereafter referred to as the “Sandias”) includes 

914 square kilometers (km2) (353 square miles [mi2]) of 

forest and wilderness land within the Cibola National Forest and Sandia Indian Reservation and encompasses parts of 

Bernalillo and Sandoval Counties. The Manzano Mountains 

(hereafter referred to as the “Manzanos”) includes 57.6 km2

(22.2 mi2) of designated wilderness land including parts 

of the Cibola National Forest, military facilities, and Isleta 

Indian Reservation, and encompass parts of Bernalillo, Torrance, and Valencia Counties. Because of their proximity to 

the City of Albuquerque, areas in the Sandias and Manzanos 

have been developed over many decades into wildland-urban 

interface areas where homes, businesses, roads, and watersupply systems are adjacent to fuel-rich forests with additional 

development in the area progressing rapidly. The population 

on the eastern slopes of the Sandias, referred to locally as the 

“East Mountains,” increased by 43 percent from 1990 through 

2000 though it has since slowed down (SWCA Environmental 

Consultants and others, 2006). Many of the watersheds on the 

western slopes of the Sandias drain directly through the City 

of Albuquerque. Watersheds and communities of the Sandias, 

Manzanos, and surrounding areas are vulnerable to several 

potential threats including wildfire and postwildfire hydrologic 

hazards.

Elevation in the study area ranges from about 

1,621 meters (m) (5,318 feet [ft]) at the base of the west slope 

of the Sandias north of the City of Albuquerque to 3,252.9 m 

(10,672 ft) at the summit of Sandia Peak. The Sandias consist of a granite core that is overlain on the eastern slopes 

by eastern dipping, Pennsylvanian-aged sedimentary beds 

(Hawley and Haase, 1992). Mean annual precipitation ranges 

from about 22.8 centimeters (cm) (9 inches [in.]) near the Rio 

Grande at the base of the mountains to about 68 cm (27 in.) at 

Sandia Peak (Bonnin and others, 2004).

Forested areas generally are denser and cover more 

area on the eastern slopes of the mountainous areas than the 

western slopes, due in part to the steeper (and in some cases 

very sheer) western slopes that preclude development of dense 

woodlands. Typical of mountainous terrain, the vegetation 

types tend to follow elevation gradients. Vegetation in the lowest elevation areas (below approximately 2,000 m (6,562 ft) 

on the eastern side of the study area consists of primarily short 

grass prairie, whereas the western low land is dominated by 

Chihuahua semidesert grassland. At the mid elevations (2,000 

to 2,200 m [6,562 to 7,218 ft]), these vegetation zones start to 

give way to pinyon pine (pinus edulis) and juniper (juniperus 

monosperma) woodlands on the eastern and western flanks 

of the mountains. At the mid-high elevations (approximately 

2,200 to 2,400 m [7,218 to 7,874 ft]), ponderosa pine (pinus 

ponderosa) forests dominate, with gamble oaks (Quercus gambelii). Eventually, at the highest elevations (more than 3,000 m 

[9,842 ft]) the ponderosa forest gives way to a spruce (picea) 

and fir (abies) mixture, primarily present on the eastern slopes 

(Julyan and Stuever, 2005).

The Manzanos are similar to the Sandias in terms of 

north-south orientation and vegetation; however, the Manzanos are much longer, extending nearly 65 kilometers (km; 

40 miles [mi]) and slightly lower in elevation reaching only 

3,178 m (10,426 ft). There are several small communities on 

the eastern slopes of the Manzanos but the range generally is 

less densely populated than the Sandias. The Manzanos are 

separated from the Sandias by Interstate-40 on figure 1. I-40 

follows the east-west trending Tijeras Canyon which is structurally controlled by a fault.

The Sandias have not experienced any large wildfires in 

recent years. The latest fire of substantial size was in 2011, 

and burned only 42 acres (Short, 2014). The Monitoring 

Trends in Burn Severity (MTBS) dataset (U.S. Geological Survey, 2013) maps all fires greater than 1,000 acres 

and dates back to 1984 (Eidenshink and others, 2007). This 

dataset indicates no fires within the Sandias during the period 

from 1984 to 2011. The lack of large fires on this landscape 

has resulted in a condition where abundant dry fuel remains 

on the ground. In the event of an ignition, this fuel source can 

contribute to increased fire intensity, wildfire size, or both. 

Such a high-intensity fire would leave the areas in the Sandias 

vulnerable to postwildfire hydrologic hazards such as debris 

flows. Although vegetation cover is less dense and fuel loads 

are lower on the western side of the Sandias, postwildfire 

hazards are still a concern because of the proximity to the 

City of Albuquerque. The Manzano Mountains have experienced several large wildfires over the past 10 years but there 

are large tracks of unburned forested lands remaining. A study 

to determine likely locations and sizes of postwildfire debris 

flows would facilitate prewildfire hazardous fuels mitigation, 

such as forest thinning, prescribed burning, and infrastructure 

stabilization by resource managers. 

Methods and Approach

The hazard assessment presented in this report was created by combining the results of three different wildfire hazard 

assessment models. Postwildfire debris flows are the primary 

focus of this assessment. The postwildfire debris flows are 

modeled as probabilities and expected volumes in response to 

a design storm according to the postwildfire debris-flow models developed by the USGS (Cannon and others, 2010). The 

debris-flow assessments rely upon a measure of burn severity, 

which was estimated using the USFS FlamMap fire-behavior 

model (Finney, 2006). Prefire assessment requires an estimation of where fires will occur or a burn probability for each 

location. Burn probabilities were estimated using the USFS 

FSim burn probability model (Finney and others, 2011). The 

analysis carried out with each of these three models is discussed in detail in appendix 1.
4 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

Figure 1. Location of study area.

I 25

I 40

Sandia Peak

RIO ARRIBA COUNTY

SOCORRO COUNTY

COLFAX COUNTY

SAN MIGUEL COUNTY

SANDOVAL COUNTY

TORRANCE COUNTY

TAOS COUNTY

LINCOLN COUNTY

GUADALUPE COUNTY

SAGUACHE COUNTY

MORA 

COUNTY

DE BACA COUNTY

LAS ANIMAS COUNTY

PUEBLO COUNTY

SANTA FE COUNTY

HUERFANO COUNTY

COSTILLA COUNTY

ARCHULETA COUNTY CONEJOS COUNTY

CIBOLA 

COUNTY

BERNALILLO COUNTY

VALENCIA COUNTY

HINSDALE 

COUNTY

CHAVES COUNTY

MINERAL 

COUNTY

CUSTER COUNTY

RIO GRANDE COUNTY ALAMOSA 

COUNTY

GUNNISON 

COUNTY

FREMONT COUNTY

MCKINLEY COUNTY

HARDING 

COUNTY

LA PLATA COUNTY

QUAY COUNTY

LOS ALAMOS 

COUNTY

Sources: Esri, DeLorme, HERE, TomTom, Intermap, increment P Corp.,

GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL,

Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), swisstopo,

and the GIS User Community

107° 106° 105°

38°

37°

36°

35°

34°0'N

EXPLANATION

Study area

Rio Grande Waterfund

Large fire simulation system

(FSim) project area 

FSim

project

area

0 25 50 MILES

0 25 50 KILOMETERS

Sand ai s

Manzanos

NEW MEXICO

COLORADO

TEXAS

ARIZONA

UTAH
Methods and Approach 5

The final, integrated debris-flow hazard index for each 

basin analyzed was created by combining the postwildfire 

debris-flow hazards (probability and volume) with an average 

annual burn probability value for each basin. The probability and volume of the postwildfire debris flows were calculated assuming that a rainfall event of a specified intensity 

(43 mm/30 minutes) occurs after the wildfire. The rainfall 

event chosen is the intensity of a 5-year, 30-minute rainfall 

event (Bonnin and others, 2004). The postwildfire debris-flow 

probability and volume calculations also required an estimate 

of the subbasin percentage and total area of moderate and high 

severity fire; these factors were estimated using FlamMap.

The use of the FlamMap fire-behavior model in this 

analysis should produce a more realistic representation of the 

geographic distributions of burn severity across the landscape 

than studies that used vegetation distributions alone (Elliot and 

others, 2012), because the FlamMap simulation will incorporate 

the effects of slope and fuel moisture that vegetation distributions alone cannot capture. The incorporation of the FSim 

burn-probability simulation results will bring in the important 

aspect of where fires are most likely to occur in these currently 

(2014) unburned landscapes. Other efforts that have used FSim 

to focus on capturing spatial heterogeneity of fire likelihood 

and behavior have been limited in the sophistication regarding 

potential watershed effects (for example, Scott and others, 2012; 

Thompson and others, 2013); therefore, the modeling efforts 

presented in this report present a step forward in combining 

spatial wildfire modeling with debris-flow modeling to inform 

assessment and mitigation efforts. A version of the model interactions for this study is shown using a flow chart in figure 2.

Modeling Extent

The landscape size needed to appropriately model burn 

probabilities is larger than those commonly used for debris-flow 

assessments, because of the nature of probabilistic fire-spread 

models. Fires that ignite in remote areas outside the study area, 

but spread into the study area, as well as those that ignite within 

the study area, needed to be accounted for; therefore, the FSim 

project area includes the entire Rio Grande Water Fund boundary as well as the Sandia and Manzano Mountains (fig. 1). The 

FSim project area includes a buffer of 15 km beyond the Rio 

Grande Water Fund boundary to allow for fires to burn onto 

the study area. The FSim project area is 30,500 km2 in size 

(fig. 3, located at http://pubs.usgs.gov/sir/2014/5161/downloads/

sir2014-5161_fig03.pdf).

Figure 2. Model interactions.

Historic fire 

weather

LANDFIRE 

fuels data

FSim

burn 

probability

FireFamily

Plus

FlamMap

fire 

behavior

Combined hazard 

ranking

Debrisflow 

probability

Historic fire 

occurrence

Fire danger indices 

and weather

Crown-fire

potential

Fire occurrence

and weather

Debris-flow

probability and volume

Burn

probability

Fire model inputs

Fire models 

Storm 

intensity

Soil

properties

Topography

Debrisflow 

volume

Debris-flow models 

Debris-flow model inputs
6 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

The debris-flow modeling extent was selected on the 

basis of three criteria: terrain, simulated burn severity, and 

precipitation patterns. The postwildfire, debris-flow models are 

designed for application in mountainous areas; therefore, the 

boundaries of the model were designed not to extend beyond 

the break in slope at the base of the Sandias and Manzanos 

on all sides. On the west face of the Sandias, the break in 

slope was clearly distinct, whereas on the eastern slopes, 

some amount of interpretation was necessary. Finally, NOAA 

Atlas 14 (Bonnin and others, 2004) isohyets were considered 

because they are indicative of areas with similar rainfall patterns; rainfall is a strong driver of debris flow probability in 

the postwildfire, debris-flow model.

Modeling Results

FlamMap Fire Behavior Simulation Results

The primary vegetation types covering the area modeled 

are low shrub and grasslands. The simulated fire activity in 

these types of vegetation is surface fire, rather than crown fire. 

The results of the FlamMap simulation (fig. 4) show passive 

and active crown-fire activity only in locations where there are 

vegetation patterns that can support crown fires, such as timber. 

Crown-fire activity was predicted only for 12 percent of the 

study area, and occurred in timber lands (fig. 4). Timber fuel 

models (appendix 1) covered approximately 15 percent of the 

study area. Within these areas, crown-fire activity was predicted 

for more than 75 percent of the forested fuels. Seventy-five 

percent crown-fire activity is comparable to the actual moderate 

and high burn severities in forested areas seen in the Big Spring 

and Trigo fires, which were used for calibration of the model 

(appendix 1). In the area burned by the Big Spring fire, 75 percent of the area classified as a timber fuel model (appendix 1) 

burned severely (MTBS categories 3 and 4). For the Trigo fire, 

61 percent of the areas classified as a timber fuel model (appendix 1) burned severely (MTBS categories 3 and 4).

Burn Probability Modeling Results

The FSim annual burn probabilities vary across the entire 

landscape from 0.00004 to 0.01, with a mean burn probability 

of 0.0023 (fig. 5, located at http://pubs.usgs.gov/sir/2014/5161/

downloads/sir2014-5161_fig05.pdf). The FSim model outputs 

result in an average of 4.3 large fires (greater than 250 acres) 

per season, and an average of 28,500 acres burned per season. 

The burn probabilities are greatest in the valley bottoms, 

where fuel models with rapid rates of spread lead to large fire 

sizes. For the entire FSim project area, the burn probabilities 

are highest southeast of Los Alamos. In the Sandias and Manzanos, burn probabilities generally are highest in the eastern 

part of the study area, in the grass and shrub fuel models, 

which have higher rates of spread than forested fuel models.

Outside of the grasslands, there are two distinct areas of 

high burn probabilities on the western slopes of the Sandias 

and Manzanos. The most predominant area is north of I-40, 

and east of Albuquerque and the North Valley. This area has 

not experienced any large fires (greater than 250 acres) since 

1992; however, given the current climate and fuels configuration of the area, this island is now more likely to experience large wildfires than other areas in close proximity. The 

remaining area of high burn probability is west of Torreon, 

in the high elevation shrub vegetation. The study area has 

experienced three fires larger than 1,000 acres since 1984 in 

the mid-elevation timber: the Big Springs, Trigo, and Ojo 

Peak fires (fig. 3). It is important to note that the mid-elevation 

timber fuel areas have a lower burn probability relative to the 

adjacent shrubs and grass vegetation. Because of the difficulty 

of suppressing fires in remote timbered areas, fires that do get 

established in areas with these characteristics tend to last a 

long time and therefore have the potential to burn large areas 

of land.

Debris-Flow Probability and Volume Estimates

Because the debris-flow model solves for debris-flow 

hazards as a function of steep terrain, and burn severity in 

part, the locations of the greatest debris-flow hazards in the 

study area correlate with the areas with steepest slopes and 

simulated fire behavior of passive or active crown fire (figs. 4 

and 6, located at http://pubs.usgs.gov/sir/2014/5161/downloads/

sir2014-5161_fig06.pdf). To aid in discussion of model results, 

the major drainages within the modeled area were delineated 

into 972 subbasins, each 11 km2 or less. The subbasin areas 

delineated range in size from 0.18 to 11 km2 and average 1.8 

km2. The maximum size of 11 km2 was used because that 

is the largest sized basin that is represented with reasonable 

confidence in the original debris-flow database used to generate the debris-flow model (Gartner and others, 2005). The total 

area encompassed by the 972 subbasins is 2,620 km2.

Analysis of debris-flow probabilities for this study area 

were calculated in response to a 5-year, 30-minute rainfall event of 43 millimeters (mm; 1.71 in.), and a 10-year, 

30-minute rainfall event of 52 mm (2.04 in.). The rainfall is 

assumed to have occurred within the first 3 years of the fire. 

High debris-flow probabilities reflect the combined effects of 

drainage basins being nearly completely burned at high and 

moderate severities and having steep slopes.

The results for a 5-year recurrence interval, 30-minute 

rainfall event were chosen for the map presentation because 

these results exhibited the best differentiation among the 

drainage basins and best highlighted the highest probability 

response drainage basins. Across the entire study area, the 

probabilities of debris flows in response to the 5-year recurrence interval, 30-minute rainfall event range from less than 5 

to greater than 95 percent. Debris-flow probabilities average 

24 percent with 75 subbasins, or 8 percent of all basins, having 

debris-flow probabilities greater than 80 percent.
Modeling Results 7

Figure 4. FlamMap simulation results.

106°50' 106°40' 106°30' 106°20' 106°10' 106°00' 105°50'

35°20'

35°10'

35°00'

34°50'

34°40'

34°30'

34°20'

Tajique

Torreon

Manzano

Belen

Tijeras

Sandia Park

Edgewood

Corrales

Cedar Crest

Bernalillo

Los Lunas

Sandia Peak

Rio Rancho

Albuquerque

South Valley

North Valley

Tajique

Torreon

Manzano

Belen

Tijeras

Sandia Park

Edgewood

Corrales

Cedar Crest

Bernalillo

Los Lunas

Sandia Peak

Rio Rancho

Albuquerque

South Valley

North Valley

Crown-fire activity value—

Simulated wildfire behavior 

as modeled by the fire 

behavior model FlamMap

Surface fire

Passive crown fire

Active crown fire

Study area

Sandia Mountains peak

EXPLANATION

Rio Grande

Rio Grande

Abo Arroyo

Galisteo Creek

Rio Puerco

Tijeras Arroyo

Palo Duro Canyon

Arroyo Seca

Jemez R

Jemez Canyon

Reservoir

Study 

area

NEW MEXICO

Albuquerque

Universal Transverse Mercator, Zone 13 North

North American Datum of 1983

0 5 10 MILES

0 5 10 KILOMETERS

Manzano 

Mountain 

Area

East 

Mountain 

Area

Ortiz 

Mountains 

Area

Sandia 

Mountain 

Area

Los Pinos 

Mountain and 

Chupadera 

Area

Manzanitas 

and 

Northern 

Manzano 

Mountain 

Area
8 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

The probability of debris flows are strongly related to 

rainfall intensity. When comparing results between the 5- and 

10-year recurrence interval rainfall events, the subbasin total 

debris-flow probabilities increase by an average of 100 percent 

across the study area. The number of subbasins with debrisflow probabilities greater than 80 percent is 179 percent higher 

or 21.5 percent of all subbasins when looking at the 10-year 

recurrence interval rainfall event as compared to the 5-year 

event. The 10-year recurrence interval model results were not 

depicted in a plate because the hazards are uniformly higher 

than the 5-year event hazards and are therefore not useful in 

terms of hazard prioritization compared to the 5-year recurrence interval plates. A breakdown of the debris-flow probabilities by subbasin in response to the 5- and 10-year recurrence 

interval, 30-minute rainfall events is summarized in table 1 

and figure 7.

Estimated debris-flow volumes range from less than 30 to 

greater than 100,000 cubic meters (m3) and average more than 

3,000 m3. Stream reaches draining the delineated subbasins 

are shown on figure 6 as “drainages within study areas that 

can be affected by the combined effects of debris flows from 

upstream drainages and side tributaries.”

Hazard Assessment

The 972 basins have been grouped together into six 

geographic areas to facilitate ease of discussion. From north to 

south, the six geographic areas are the Ortiz Mountains area, 

the Sandia Mountains area, the East Mountain area, the Manzanitas and Northern Manzano Mountain area, the Mazano 

Mountain area, and the Los Pinos Mountain and Chupadera 

area (fig. 6).

Ortiz Mountains Area

The Ortiz Mountains area (fig. 6) is the most northern 

section of the study area. The Ortiz Mountains area encompasses the Ortiz Mountains closest to Galisteo Creek and 

the smaller San Pedro and South Mountains, both of which 

are south of the Ortiz Mountains. Basin areas delineated in 

the Ortiz Mountains area total 363 km2 or 13.85 percent of 

the total basin areas delineated in the study area. Outside of 

the isolated peaks, the area has fairly gentle topography and 

low burn severity (fig 4). This geographic area has a total of 

131 delineated basins.

The debris-flow probabilities (fig. 6) in response to the 

modeled 5-year, 30-minute rainfall event in the Ortiz Mountains area range from less than 5 percent to greater than 90 percent and average about 26 percent. The estimated debris-flow 

Table 1. Summary of debris-flow probability values by subbasin in response to 5- and 10-year 

recurrence interval, 30-minute rainfall events.

Debris-flow 

probability, 

in percent

5-year recurrence, 30-minute 

duration rainfall

10-year recurrence, 30-minute 

duration rainfall Percent increase in 

probability from 5-year 

event to 10-year event

1.71 inches (43 millimeters) 2.04 inches (52 millimeters)

Number of 

subbasins

Percent of 

subbasins

Number of 

subbasins

Percent of 

subbasins

80–100 75 8 209 21.5 179

60–80 104 10 184 19 77

40–60 144 15 220 23 53

20–40 239 25 336 34.5 41

0–20 410 42 23 2 -94

Figure 7. Comparison of debris-flow probability subbasin totals 

for 5- and 10-year recurrence interval, 30-minute rainfall events

0

50

100

150

200

250

300

350

400

450

0–20 20–40 40–60 60–80 80–100

Number of subbasins

Debris-flow probability, in percent

10-year recurrence interval rainfall

5-year recurrence interval rainfall

EXPLANATION
Hazard Assessment 9

volumes (fig. 6) in this area range from less than 30 m3 to 

nearly 60,000 m3 and average about 6,400 m3. The Ortiz Mountains area only contains five subbasins that have debris-flow 

probabilities greater than 80 percent and, as with the rest of the 

study area, the greatest hazards are on the steepest slopes of the 

mountainous areas. The Ortiz gold mine (not shown) is located 

on the east-facing slopes of the Ortiz Mountains about 2 miles 

from the summit within one of these five subbasins.

Sandia Mountains Area

The Sandia Mountains area (fig. 6) is just east of the 

Albuquerque metropolitan area. The Sandia Mountains area 

stretches from the northern extent of the Sandias south to 

Tijeras Arroyo. The southernmost, west-facing basins drain 

into the Albuquerque metropolitan area. The eastern slopes are 

moderately populated with widely distributed rural housing 

and small communities. Basin areas delineated in the Sandia 

Mountains area total 395.7 km2 or 15.1 percent of the total 

basin areas delineated in the study area. The topography of 

this area is characterized by a mostly north-south trending 

mountain range, which has steep and heavily dissected slopes 

on the west-facing flank and somewhat shallower slopes on 

the east-facing flank. The simulated burn severity based on 

crown-fire activity (fig. 4) is high in patchy areas around the 

mid slopes of the mountains on all sides but particularly on the 

eastern slopes where the forested areas are denser and extend 

farther down the slopes. This geographic area has a total of 

162 delineated basins.

The debris-flow probabilities (fig. 6) in response to 

the modeled 5-year, 30-minute rainfall event in the Sandia 

Mountains area range from less than 5 percent to greater than 

90 percent and average about 42 percent. There are 18 subbasins in this area that have debris-flow probabilities greater 

than 80 percent, most of which are on west-facing slopes 

and upstream from the Albuquerque metropolitan area. Four 

small subbasins with debris-flow probabilities greater than 

80 percent are upstream tributaries to Las Huertas Creek along 

Highway 165 upstream from a section of the community of 

Placitas. Two subbasins with debris-flow probabilities between 

60 and 80 percent terminate along Highway 14 within the 

community of Cedar Crest. The estimated debris-flow volumes 

(fig. 6) in this area range from less than 30 m3 to more than 

70,000 m3 and average around 10,000 m3.

Manzanitas and Northern Manzano Mountain 

Area

The Manzanitas and Northern Manzano Mountain area 

(fig. 6) is the area just south of the Sandia Mountain area and 

Tijeras Arroyo. Basin areas delineated in the Manzanitas and 

Northern Manzano Mountain area total 290 km2 or 11.1 percent 

of the total basin areas delineated in the study area. The topography of this area is characterized by a moderately high zone on 

the east side that is dissected by numerous complex drainages 

on the western side. Areas of high simulated burn severity based 

on crown-fire activity (fig. 4) are minimal and occur almost 

exclusively on the eastern slopes where, like in the Sandias, the 

forested areas are denser and extend farther down the slopes. 

This geographic area has a total of 136 delineated basins.

The debris-flow probabilities (fig. 6) in response to the 

modeled 5-year, 30-minute rainfall event in the Manzanitas 

and Northern Manzano Mountain area range from less than 

10 percent to greater than 90 percent and average around 

45 percent. There are 10 subbasins in this area that have 

debris-flow probabilities greater than 80 percent, all of which 

are located on tributaries to major trunk channels in the area. 

In the northern part of this geographic area, some of the high 

debris-flow probability subbasins are upstream from military 

facilities associated with the Kirtland Air Force Base. The 

estimated debris-flow volumes (fig. 6) in this area range from 

35 m3 to nearly 50,000 m3 and average around 6,800 m3.

East Mountain Area

The East Mountain area (fig. 6) covers the area south of 

the Ortiz Mountains area and east of the Sandia and Manzanitas and Northern Manzano Mountains areas. It is characterized 

by the gentlest slopes of all the geographic areas with only a 

small patch of simulated high burn severity based on crownfire activity (fig. 4) in the southwest corner near the Manzanos. 

These factors lead to the area having lower debris-flow probabilities than the other areas. This geographic area has a total 

of 131 delineated basins. Basin areas delineated in the East 

Mountain area total 388 km2 or 14.8 percent of the total basin 

areas delineated in the study area.

The debris-flow probabilities (fig. 6) in response to the 

modeled 5-year, 30-minute rainfall event in the East Mountain 

area range from just over 8 percent to greater than 60 percent 

and average around 15 percent. There are no subbasins in this 

area that have debris-flow probabilities greater than 80 percent 

and only two subbasins with debris-flow probabilities between 

60 and 80 percent. The estimated debris-flow volumes (fig. 6) 

in this area range from 27 m3 to greater than 10,000 m3 and 

average around 1,100 m3.

Manzano Mountain Area

The Manzano Mountain area (fig. 6) encompasses the 

main body of the Manzano Mountains. The topography of the 

Manzano Mountain area is characterized by steep slopes draining the eastern and western flanks of the north-south trending 

mountain range. This area includes a fairly high concentration 

of simulated high burn severity based on crown-fire activity 

(fig. 4) and, as with the Sandias, it is located mostly on the 

eastern slopes. The Manzano Mountain area is the largest of 

the geographic areas with a total of 247 delineated basins. 

Basin areas delineated in the Manzano Mountain area total 

383 km2 or 26.05 percent of the total basin areas delineated in 

the study area.
10 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

The debris-flow probabilities (fig. 6) in response to the 

modeled 5-year, 30-minute rainfall event in the Manzano 

Mountain area range from just under 8 percent to nearly 

100 percent and average around 40 percent. There are 40 subbasins in this area that have debris-flow probabilities greater 

than 80 percent, which generally are associated with the 

steepest drainages along the crest of the mountain range 

occurring on the eastern and western sides. Fortunately, there 

is very little development as of yet on the western slopes of 

the southern Manzanos that would be impacted by the many 

basins with debris-flow probabilities greater than 80 percent 

that drain those slopes; however, on the eastern slopes of the 

Manzanos the communities of Tajique, Torreon, and Manzano 

are all located within 2 to 5 miles downstream from subbasins 

with debris-flow probabilities greater than 80 percent. The 

estimated debris-flow volumes (fig. 6) in this area range from 

27 m3 to nearly 35,000 m3 and average around 4,800 m3.

Los Pinos Mountain and Chupadera Area

The Los Pinos Mountain and Chupadera area (fig. 6) 

consists of the smaller mountains and mesas in the southern 

parts of the study area and is separated from the bulk of the 

Manzano Mountains by Abo Arroyo. The topography of the 

Los Pinos Mountain and Chupadera area is characterized more 

by mesas and plateaus than steep mountain slopes. The simulated burn severity based on crown-fire activity (fig 4) in most 

of this area is low with the exception of small high-severity 

patches along the highest ridges. This area has a total of 

165 delineated basins. Basin areas delineated in the Los Pinos 

Mountain and Chupadera area total 500 km2 or 19.1 percent of 

the total basin areas delineated in the study area.

The debris-flow probabilities (fig. 6) in response to the 

modeled 5-year, 30-minute rainfall event in the Los Pinos 

Mountain and Chupadera area range from nearly 11 percent to 

greater than 85 percent and average around 30 percent. There 

are only 2 subbasins in this area that have debris-flow probabilities greater than 80 percent, and 11 subbasins with debrisflow probabilities from 60 to 80 percent. The development in 

these areas ranges from sparse to nonexistent. The estimated 

debris-flow volumes (fig. 6) in this area range from 27 m3 to 

100,000 m3 and average around 10,000 m3.

Integrated Relative Debris-Flow 

Hazard Rankings

Debris-flow hazards from a given subbasin can also be 

represented by an Integrated Relative Debris-Flow Hazard 

Index that is based on a combination of debris-flow probability, estimated volume of debris flow, and average burn 

probability for each basin. For example, the most hazardous 

subbasins will have the highest probabilities of experiencing 

a fire in some part of the subbasin, the highest probabilities of 

debris-flow occurrence, and the largest estimated volumes of 

debris-flow material. Slightly less hazardous would be subbasins that show a combination of high probabilities of burning 

but only low probabilities of moderate-sized debris flow or 

perhaps subbasins with moderate probabilities of very large 

debris flows but only low probabilities of burning.

To compute an integrated debris-flow hazard for each 

basin, it was necessary to generate a single burn probability 

value for each basin. To generate the individual basin burn 

probability values, the results of the FSim burn probability 

simulation were averaged for each basin analyzed using the 

continuous parameterization method to create an average burn 

probability index (see appendix 1). Although the basin-average 

burn probability index values do not actually quantify the likelihood of an entire given basin burning in 1 year, they provide a 

measure of burn likeliness that is useful for prioritizing hazards 

by basins. This technique allowed for a synoptic view of conditions throughout the entire study area, which could be used 

to identify specific subbasins that might pose a higher risk of 

experiencing a wildfire somewhere within the subbasin area.

The results of the basin-average burn-probability indices are shown in figure 8, located at http://pubs.usgs.gov/

sir/2014/5161/downloads/sir2014-5161_fig08.pdf. These results 

can be compared directly to the FSim burn probability output 

as shown in figure 5. By averaging over the entire basin, the 

within-basin spatial information provided by the burn probability values is lost; however, the comparisons of figure 8 with 

figure 5 show that the basin averaged values are not unreasonable compared to the spatially distributed values.

Computation of the integrated relative debris-flow 

hazard index for each subbasin was a two-step process. For 

each subbasin, the estimated debris-flow probability was first 

multiplied by the average burn probability index to produce 

a debris-flow likelihood index. In the second step, the debrisflow likelihood index for each subbasin was then multiplied by 

the estimated debris-flow volume for that subbasin to produce 

an integrated debris-flow hazard index.

The integrated debris-flow hazard index values were 

then ranked from 1 to 972, with 1 representing the highest 

and 972 representing the lowest integrated debris-flow hazard 

index value. This integrated debris-flow hazard index ranking 

identifies a possible range of responses from basins with the 

highest probabilities of producing debris flows with the largest 

volumes in areas that are most likely to experience fires to 

basins with the lowest probabilities of producing debris flows 

with the smallest volumes in areas that are the least likely to 

experience fires.

The wildfire and debris-flow modeling results for the 

972 subbasins in the study area are shown in figure 9 using 

a scatterplot of conditional (based on the 5-year recurrence 

interval, 30-minute rainfall event) debris-flow volumes with 

debris-flow likelihood indices (postwildfire debris-flow probability multiplied by annual burn probability) for all subbasins. 

The 972 subbasins are divided into three categories based 

on their integrated relative debris-flow hazard rankings: top 

2 percent, top 10 percent, and lowest 90 percent of subbasins. 
Limitations of Hazard Assessment 11

Reference lines for common percentile breakdowns also are 

shown on the scatterplot of conditional volumes with annual 

probabilities of debris flows for all modeled subbasins.

Most of the subbasins with the highest integrated debrisflow hazard index rankings are in the steepest parts of the 

Sandias and Manzanos and contain substantial areas of high, 

simulated burn severity and therefore high basin-average, 

annual burn probability indices. Subbasins in the Ortiz Mountains and the Los Pinos Mountain and Chupadera areas have 

generally low integrated debris-flow hazard indices with a few 

scattered, high integrated debris-flow hazard index subbasins. 

Subbasins in the East Mountain geographic area have exclusively moderate to low integrated debris-flow hazard indices.

Nineteen subbasins are contained in the upper 2 percent 

of integrated debris-flow hazard indices rankings. These subbasins include five subbasins on the west-facing slopes of the 

Sandias, four of which have downstream reaches that lead 

into the outskirts of the City of Albuquerque (see inset on 

figure 8). Of the remaining 14 subbasins in the upper 2 percent 

of integrated debris-flow hazard indices rankings, 12 are 

located along the highest and steepest slopes of the Manzano 

Mountains, largely on the western slope; however, one of 

these subbasins is approximately 5 miles upstream from the 

community of Tajique and another is several miles upstream 

from the community of Manzano, both on the eastern slopes of 

the Manzanos. The Ortiz Mountains and Cupadera geographic 

area also each have one basin in the top 2 percent of integrated 

debris-flow hazard index rankings including the subbasin in 

which the Ortiz gold mine is located in the Ortiz Mountains 

geographic area.

The top 5 percent of integrated debris-flow hazard index 

rankings includes 28 subbasins in addition to the 19 from the 

top 2 percent. Four of these subbasins also are on the westfacing slopes of the Sandias, with downstream reaches that 

eventually lead into the City of Albuquerque. Other subbasins 

in the top 5 percent have basin outlets within the communities 

of Sandia Park and Cedar Grove on the eastern slopes of the 

Sandias. Two communities on the eastern slopes of the Manzanos could be threatened by debris flows in subbasins that 

have integrated debris-flow hazard indices in the upper 5th 

percentile. One high, integrated debris-flow hazard index subbasin outlets within the community of Manzano, one outlets 

several miles upstream from the community of Torreon, and a 

third is approximately 5 miles upstream from the community 

of Tajique.

Limitations of Hazard Assessment

The use of models for forecasting uncertain events necessarily comes with limitations and potential errors, particularly 

when multiple models are combined. With respect to both 

wildfire and debris-flow modeling, a limited empirical basis 

for model calibration purposes may not be capturing the full 

range of possible outcomes, highlighting a need for updating models as new observations are made. The assumption 

of uniform rainfall intensity across the study area and the use 

of a single Remote Automated Weather Station may not be 

capturing fine-scale variation in factors influencing wildfire 

Figure 9. Scatterplot of conditional debris-flow volume with debris-flow likelihood index for all modeled subbasins.

0

20,000

40,000

60,000

80,000

100,000

120,000

Conditional debris-flow volume, in cubic meters

Debris-flow likelihood index

Conditional volumes and likelihood index values of debris flows for all subbasins

50th percentile

80th percentile

90th percentile

98th percentile

Top 2 percent of integrated hazard ranking

Top 10 percent of integrated hazard ranking

Remaining subbasin

EXPLANATION

0 0.001 0.002 0.003 0.004 0.005 0.006
12 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

and postwildfire debris-flow potential. Limitations and uncertainties specific to fire modeling tools include an incomplete 

understanding of how uncertainty and errors propagate 

through models and knowledge gaps relating to crown-fire 

potential and propagation, fire-atmosphere dynamics, and firefuel interactions (Scott and others, 2012). Further, the spatial 

aggregation of basin average burn probabilities for continuous 

parameterization purposes likely is masking finer scale variation because of topography and local fuel conditions. These 

factors may, in part, explain why the subbasin burn probabilities index is not a strong driver of the integrated relative 

debris-flow hazard rankings.

The probability of debris flow increases with increasing 

recurrence interval (larger, less frequent) design storm. Larger, 

less frequent storms (for example, a 50-year recurrence interval rainfall event) are likely to produce larger debris flows, 

whereas smaller, more frequent storms (for example, a 1-year 

recurrence rainfall) could also trigger debris flows, but they 

would likely be smaller. Higher probabilities of debris flows 

than those shown on figure 6 may exist within any part of the 

drainage basins. Because most rainstorms will not be large 

enough to affect the entire burn area, debris flows will not be 

produced from all drainage basins during a given storm.

It is important to note that the maps shown in figures 6 

and 8 do not categorize those areas that can be affected by 

debris flows as the material moves downstream from the basin 

outlets (Cannon and others, 2010). The maps only categorize 

those areas from which debris will be moved.

The variables included in the debris-flow models and 

used in this assessment are considered to directly affect debrisflow generation in the intermountain Western United States. 

Conditions other than those used in the models (for example, 

the amount of sediment stored in a canyon) could also affect 

debris-flow production. Data necessary to evaluate such 

effects, however, are not readily available.

The continuous parameterization technique, while efficient for analytical purposes, may be masking localized variation in conditions affecting either wildfire potential or debrisflow potential. Fine-scale variability in surface characteristics 

that are not captured by the independent variables used in the 

model (such as local sediment supply or differences in infiltration rates) may dramatically affect debris flow generation and 

propagation.

The debris-flow model is considered valid for conditions 

that typically persist in a burned watershed for one to several 

years after a fire. The model does not account for variations in 

the timing of landscape recovery after a fire. The rate of recovery of individual landscapes will vary particularly with respect 

to post fire rainfall amount, timing, and intensity.

The debris-flow analysis is based on simulated postwildfire conditions and does not account for potential mitigating 

effects of prefire treatments such as forest thinning or prescribed burning. The analysis does serve to highlight, especially by the stream segment analysis, those parts of a basin 

with an increased debris-flow probability based on physical 

characteristics. The information provided by this study should 

be considered together with local expertise and information to 

guide mitigation and restoration planning.

This study was initiated to help select ideal locations 

for watershed restoration that could have the best return on 

investment. The study provides information on which watersheds might constitute the most serious, potential, debris-flow 

hazards in the event of a large-scale wildfire and subsequent 

rainfall in the Sandia and Manzano Mountain areas. The maps 

and geospatial data provided with this report may be used to 

prioritize areas where forest thinning or other protective measures may be needed prior to wildfires within these drainage 

basins, their outlets, or areas downstream from these drainage 

basins to help reduce potential burn severities. This assessment 

evaluates only postwildfire debris flows and does not consider 

hazards associated with flash floods; such hazards may remain 

for many years after a fire.

Future Considerations for Prewildfire 

Assessments of Postwildfire Hazards

This study has extended applications of several different 

models beyond what they were originally designed for. The 

debris-flow models used in this report were designed for postfire assessments; for this study, they were applied before any 

fire occurred. The FlamMap model was designed to model fire 

behavior; for this study, it was used to estimate burn severity. 

The FSim model was developed to present spatially specific 

burn probabilities; for this study, those values were averaged 

over basin areas and combined with outputs from the debrisflow and FlamMap models to compute an integrated debrisflow hazard for each basin. These extensions of the original 

model applications are not ideal but were used because of the 

need for prioritization of prefire hazards. Individually these 

models are difficult to verify and the errors associated with 

combining them in the manner used here will be difficult to 

verify as well. Although this methodology represents a step 

forward in how pre-event assessments of potential hazards 

are evaluated, the methodology is still in the early stages of 

development and will continue to evolve as more studies like 

this are completed.

Summary

Debris flows are high-density slurries of water, rock 

fragments, soil, and mud that can have enormous destructive power particularly when they are fast moving. Wildfire 

can drastically increase the probability of debris flows in 

landscapes that have otherwise been stable throughout recent 

history. A prewildfire study to determine the potential for 

postwildfire debris flows in the Sandia and Manzano Mountain areas in Central New Mexico was initiated in 2013 by 

the U.S Geological Survey in cooperation with the Bernalillo 
References Cited 13

County Natural Resources Services as a part of the Rio Grande 

Water Fund. The U.S. Forest Service and The Nature Conservancy provided support for this effort principally through 

fire simulation modeling. The study was conducted to provide 

information on which subwatersheds might constitute the 

most serious, potential, debris-flow hazards in the event of a 

large-scale wildfire and subsequent rainfall in the Sandia and 

Manzano Mountain areas. The U.S. Forest Service fire behavior model FlamMap was used to estimate the burn severity 

likely to occur in unburned areas of the Sandia and Manzano 

Mountains. The U.S. Forest Service large fire simulation system FSim was used to estimate the probability of fire spreading across all areas of the Sandia Mountains and beyond. The 

U.S. Geological Survey post-wildfire debris flow model was 

used to make estimates of the probabilities of debris flows and 

of volumes of material that could be removed from subbasins 

based on topography, soil characteristics, and simulated burn 

intensities.

The results of this study indicate that there is a wide 

range of postwildfire hazards present in the Sandia and Manzano Mountains areas. Those areas with the highest modeled 

hazards generally are steep areas, with ample fuel supplies. 

Some of the highest modeled hazards subbasins are upstream 

from the metropolitan area of Albuquerque and other smaller 

communities within the wildland-urban interface area.

Across the entire study area, the probabilities of debris 

flows in response to the 5-year-recurrence interval, 30-minute 

rainfall event range from less than 5 to greater than 95 percent and average 24 percent with 75 subbasins, or 8 percent 

of all basins, having debris-flow probabilities greater than 

80 percent. When comparing debris-flow probability results 

between the 5-year and 10-year recurrence interval rainfall 

events, subbasin debris-flow probabilities increase 100 percent 

across the study area. The number of subbasins with debrisflow probabilities greater than 80 percent is 179 percent higher 

or 21.5 percent of all subbasins when looking at the 10-year 

recurrence interval rainfall event as compared to the 5-year 

event. Estimated debris-flow volumes in response to the 5-year 

recurrence interval, 30- minute intensity rainfall range from 

less than 30 to greater than 100,000 m3 and average over 

3,000 m3.

The integrated relative debris-flow hazard rankings for 

each subbasin were generated by multiplying the individual 

values for debris-flow volume, debris-flow probability, and 

average burn probability for each subbasin. The integrated 

relative debris-flow hazard ranking analysis shows that most 

of the basins with the highest integrated relative debris-flowhazard rankings are in the steepest portions of the Sandia 

and Manzano Mountains. Among subbasins in the upper 

2 percentile of integrated relative debris-flow hazard rankings, are 5 subbasins on the west-facing slopes of the Sandia 

Mountains, 4 of which have downstream reaches that lead 

into the outskirts of the City of Albuquerque. The bulk of the 

remaining 14 subbasin in the upper 2 percent of integrated 

relative debris-flow hazard rankings are located along the 

highest and steepest slopes of the Manzano Mountains, largely 

on the western slope. However one is a subbasin several miles 

upstream of the community of Torreon and another is several 

miles upstream of the community of Manzano, both on the 

eastern slopes of the Manzanos.

The top 5 percent of integrated relative debris-flow 

hazard rankings includes 47 total subbasins. Four of these 

subbasins are also on the west-facing slopes of the Sandias 

with downstream reaches that eventually lead into the City 

of Albuquerque. Other subbasins in the top 5 percent have 

basin outlets within the communities of Sandia Park and 

Cedar Grove on the eastern slopes of the Sandias. On the 

eastern slopes of the Manzano Mountains, upper 5th percentile 

integrated relative debris-flow hazard subbasins outlet within 

the community of Manzano, and several miles upstream of the 

community of Tajique.

The maps in this report may be used to prioritize areas 

where forest thinning or other protective measures may be 

needed prior to wildfires within these drainage basins, their 

outlets, or areas downstream from these drainage basins to 

help reduce potential burn severities. This assessment evaluates only postwildfire debris flows and does not consider 

hazards associated with flash floods; such hazards may remain 

for many years after a fire.

Acknowledgments

Several people from multiple U.S. Geological Survey 

(USGS) Science Centers assisted in preparation of this report. 

The authors acknowledge Dennis Staley of the USGS Landslide Hazards Program and Barbara Ruddy of the USGS 

Colorado Science Center for providing excellent and helpful 

colleague reviews. Thanks are extended to Kris Verdin also of 

the USGS Colorado Science Center who provided additional 

guidance on methodology.

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Appendix
16 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

Modeling Methods and Approach

In northern New Mexico, widespread watershed restoration efforts are being undertaken to safeguard vital watersheds against the threat of catastrophic wildfire. This study 

was initiated to help select ideal locations for the restoration 

efforts in places that could have the best return on investment. 

The hazard assessment presented in this report was created 

by combining the results of three different wildfire hazardassessment models: a large fire simulation system model, a fire 

behavior model and a postwildfire debris-flow model.

Postwildfire debris flows are the primary focus of this 

assessment. The postwildfire debris flows are modeled as 

probabilities and expected volumes in response to a design 

storm according to the postwildfire debris-flow models developed by the U.S. Geological Survey (USGS) (Cannon and 

others, 2010). The debris-flow assessments rely on a measure 

of burn severity that was estimated using the U.S. Department of Agriculture, Forest Service USFS FlamMap firebehavior model (Finney, 2006). Prefire assessment requires an 

estimation of where fires will occur or a burn probability for 

each location. The large fire simulation system known as FSim 

(Finney and others, 2011) is a burn probability model that 

has been used in numerous wildfire exposure and risk assessments at various scales (Thompson and others, 2011; Ager 

and others, 2012) and to address impacts to various resources 

(Haas and others, 2013; Thompson and others, 2013; and Scott 

and others, 2012). For this study, the USFS FSim burn probability model burn was used to estimate burn probabilities. A 

schematic for the model interactions in this study is shown in 

figure 1–1.

The final step in the project was to generate an overall 

hazard index for all basins in the study area by combining the 

modeled debris-flow hazards, which incorporate the fire-severity estimates, with the modeled burn probabilities for each 

basin. This appendix covers additional technical details of the 

modeling methods, model interactions, and model calibrations 

used in the study. The primary topics covered are the postwildfire debris-flow, FlamMap fire behavior, and FSim burn 

probability modeling efforts.

Figure 1–1. Model interactions.

Historic fire 

weather

LANDFIRE 

fuels data

FSim

burn 

probability

FireFamily

Plus

FlamMap

fire 

behavior

Combined hazard 

ranking

Debrisflow 

probability

Historic fire 

occurrence

Fire danger indices 

and weather

Crown-fire

potential

Fire occurrence

and weather

Debris-flow

probability and volume

Burn

probability

Fire model inputs

Fire models 

Storm 

intensity

Soil

properties

Topography

Debrisflow 

volume

Debris-flow models 

Debris-flow model inputs
Debris-Flow Hazard Modeling 17

Debris-Flow Hazard Modeling

A pair of empirical models was used to estimate the probability and volume of debris flows along the drainage network 

and for selected drainage basins in response to a given rainfall 

event in the study area. The model for predicting debris-flow 

probability was developed by Cannon and others (2010) using 

logistic multiple-regression analyses of data from 388 basins 

in 15 burned areas in the intermountain western United 

States. Conditions in each basin were quantified using readily 

obtained measures of basin gradient, soil properties, storm 

rainfall, and a simulated measure of areal burned extent. Statistical analyses were used to identify the variables that most 

strongly influenced debris-flow occurrence and to build the 

predictive model. Equation 1 is used to calculate debris-flow 

probability (Cannon and others, 2010):

P = e x/(1 + e x), (1) 

where

P is the probability of debris-flow occurrence in 

fractional form, and

e x is the exponential function where e represents 

the mathematical constant 2.718.

Equation 2 is used to calculate x:

x = −0.7 + 0.03(%SG30) − 1.6(R) + 

0.06(%AB) + 0.07(I) + 0.2(%C) − 0.4(LL),

where

	 %SG30 is the percent of the drainage basin area with 

slope equal to or greater than 30 percent;

R is drainage basin ruggedness, the change 

in drainage basin elevation (in meters) 

divided by the square root of the drainage 

basin area (in square meters) (Melton, 

1965);

%AB is the percentage of drainage basin area 

burned at moderate and high severity;

I is average storm intensity (the total storm 

rainfall divided by the storm duration, in 

millimeters per hour);

%C is the percent clay content of the soil 

(STATSGO, Schwartz and Alexander, 

1995); and

LL is the liquid limit of the soil (the percent 

of soil moisture by weight at which soil 

begins to behave as a liquid) (STATSGO, 

Schwartz and Alexander, 1995).

A second statistical model was used to estimate the 

volume of material that could issue from the basin mouth of a 

recently burned drainage basin in response to a given magnitude storm. This model was developed using multiple linearregression analyses of data compiled from 56 debris-flowproducing basins burned by 8 fires (Cannon and others, 2010). 

Debris-flow volume measurements were derived from records 

of the amount of material removed from sediment-retention 

basins and from field measurements of the amount of material 

eroded from the main channels within a burned drainage. Statistical analyses were used to identify the variables that most 

strongly influenced debris-flow volume. The model provides 

estimates of the volume of material that may pass through a 

drainage-basin outlet in response to a single rainfall event. The 

model has the following form:

 Ln(V)= 7.2 + 0.6(Ln(SG30)) + 0.7(AB)0.5 + 0.2(T)0.5 + 0.3, (3)

where

V is the debris-flow volume (in cubic meters);

Ln is the natural log function;

	 SG30 is the area of drainage basin with slopes equal 

to or greater than 30 percent (in square 

kilometers);

AB is the drainage basin area burned at moderate 

and high severity (in square kilometers);

T is the total storm rainfall (in millimeters); and

0.3 is a bias correction factor that changes the 

predicted estimate from a median to a 

mean value (Cannon and others, 2009; 

Helsel and Hirsch, 2002).

Values for debris-flow probability and volume were 

obtained along drainage networks using the continuous 

parameterization technique (Verdin and Greenlee, 2003; 

Verdin and Worstell, 2008). With this technique, estimates 

of debris-flow probability and volume (Cannon and others, 

2010) were obtained for every 10-meter pixel along the drainage network as a function of conditions in the drainage basin 

upstream from each pixel. The independent variable values 

can be represented as forming continuous surfaces over the 

burned area. Once the surfaces of the independent variables 

were developed, the probability and volume equations were 

solved using map algebra for each grid cell along the drainage network. This technique was developed as an alternative 

to basin-characterization approaches used previously (for 

example, Cannon and others, 2010), which require definition 

of outlets or pour points and their corresponding basins at the 

beginning of the analysis. The technique used here allows 

for a synoptic view of conditions throughout the entire study 

area, which can be used to identify specific 10-meter pixels or 

stream reaches that might pose a higher risk of debris flows; 

the technique also aids in sampling design and monitoring-site 

selection. The computations were carried out using an ArcGIS toolbox developed by the USGS (Andrew Bock, Barbara 

Ruddy, and Kristine Verdin, U.S. Geological Survey, written 

commun., 2013).

The base layer on which the continuous-parameterization 

layers are built is the 1/3-arc-second National Elevation Dataset 

(Gesch and others, 2002). This digital elevation model (DEM) 

was transformed into a projection system appropriate to central 

New Mexico (Universal Transverse Mercator [UTM], Zone 

13) and processed using standard DEM-conditioning tools in 

ArcGIS (Esri, 2011) and RiverTools (Rivix, 2012). Once the 

(2)
18 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

overland flow structure was derived (in the form of a flowdirection matrix) using the DEM, the independent variables 

driving the probability and volume equations were evaluated 

for every grid cell within the extent of the DEM.

Because of orographic effects of the mountainous terrain 

and the size of the area modeled, rainfall totals and rainfall 

intensities will vary over the extent of the burned area. For this 

study, however, the maximum rainfall amounts for each storm 

were assumed to be uniform over the entire area modeled, 

providing the most conservative or highest estimate of the 

probability and volumes of potential debris flows.

FlamMap Fire-Behavior Modeling

Burn severity is a necessary input for the debris-flow 

model (Cannon and others, 2010). Burn severity is a measure 

of the relative changes in pre- and immediate postfire vegetation cover combined with relative measures of the distribution 

of water-repellent soils (Parsons and others, 2002; Keeley, 

2009); therefore, a prewildfire assessment of potential postwildfire debris-flow activity requires some estimate of potential 

burn severity. Burn severity can be estimated using fire intensity and crown-fire metrics output by fire-behavior modeling systems as a proxy for burn severity. These fire-behavior 

modeling systems can capture spatial variability in topography, 

vegetation, and fuel characteristics, which in turn, can influence the spatial pattern and extent of basin burn severity. For 

this prewildfire debris-flow assessment, the fire-behavior model 

FlamMap was used to estimate the burn severity likely to occur 

in the study area. FlamMap was created by the U.S. Department of Agriculture Forest Service, Missoula Fire Sciences 

Laboratory (Finney, 2006), and is designed to characterize 

spatial variability in potential fire behavior under a constant set 

of environmental conditions for an entire landscape. FlamMap uses spatial information on topography and fuels, fuel 

moisture, and weather data to calculate potential fire behavior 

including crown-fire activity as well as rate of spread, flame 

length, and fireline intensity across the landscape. FlamMap 

incorporates surface fire spread (Rothermel, 1972), crown-fire 

spread (Rothermel, 1991), and crown-fire initiation (Van Wagner, 1977) models. FlamMap models the potential fire behavior 

across an entire landscape at one instant in time.

The crown-fire activity fire-behavior output from FlamMap, which is a classification of fire type as either surface 

fire, passive crown fire, or active crown fire, was used to estimate the burn severity likely in unburned areas of the study 

area. Crown fire is the movement of fire into and through the 

forest canopy: active crown fire carries continuously through 

the forest canopy, whereas passive crown fire does not carry 

continuously but burns crown fuels intermittently (that is, 

when individual trees or groups of trees burn). Areas simulated as having crown-fire activity, either passive or active, 

were interpreted as areas likely to burn with moderate to high 

severity for the purpose of the debris-flow model. Crown-fire 

activity output was chosen for two reasons. First, crownfire activity is considered representative of the actual burnseverity data calculated from remotely sensed imagery (Davis 

and others, 2010). Second, simulated fire activity in grassland 

fuel types always results in surface fire rather than crown fire, 

so burn severity is not expected to be severe; therefore, the 

results are not expected to overpredict severity (Davis and 

others, 2010).

Spatial fuel and topography information needed to run 

FlamMap was obtained from LANDFIRE (Ryan and Opperman, 2013; U.S. Geological Survey, 2013b). LANDFIRE is an 

interagency mapping program responsible for producing and 

maintaining a suite of comprehensive and consistent geospatial layers representing topographic, vegetation, fuels, and fire 

conditions across the United States. The LANDFIRE topographic layers include slope, aspect, and elevation (Ryan and 

others, 2013). The LANDFIRE fuels layers include canopy 

cover, stand height, canopy base height, canopy bulk density, 

fuel-loading model, and fire-behavior fuel model (Scott and 

Burgan, 2005). Specific methodology on the creation of the 

LANDFIRE topographic and fuels layers can be accessed at 

http://landfire.gov/.

Fire-Behavior Model Calibration

Calibration for the debris-flow assessment study area 

was based on an accuracy assessment of the modeled crownfire activity output using prewildfire LANDFIRE fuel layers 

compared to the actual burn severity from two fires that 

occurred in 2008 in the Manzano Mountains: Trigo Fire and 

Big Spring Fire. These two fires were chosen for the local 

calibration and accuracy assessment because they represent 

fires in the vicinity with potential fuel conditions and resulting burn severity that may occur in the Sandias or elsewhere 

in the study area. Burn severity data from the Trigo and Big 

Spring Fires were downloaded from the USGS Monitoring 

Trends in Burn Severity (MTBS) project (U.S. Geological 

Survey, 2013a). The MTBS burn severity layers depict severity in five thematic categories: (1) unburned to low severity, 

(2) low severity, (3) moderate severity, (4) high severity, 

and (5) increased greenness or increased postfire vegetation 

response. The MTBS severity layers are created using Normalized Burn Ratios derived from pre- and post-fire remote 

imagery that are analyzed and classified into the above thematic categories using methodology developed by USFS and 

USGS. A full description of the methodology can be accessed 

at http://www.mtbs.gov. In the area burned by the Big Spring 

fire, 75 percent of the area classified as a timber fuel model 

burned severely (MTBS categories 3 and 4). For the Trigo 

fire, 61 percent of the areas classified as a timber fuel model 

burned severely (MTBS categories 3 and 4).

The comparison between the measured burn severities 

given by the MTBS analysis and the simulated burn severities 

output by FlamMap yields four basic categories of results. The 

actual (MTBS) and simulated (FlamMap) severities either are 
Burn Probability Modeling 19

(1) in agreement that the severity is high; (2) in agreement that 

the severity is low; (3) in disagreement, with actual severities showing high severity and simulated output showing low 

severity; or (4) in disagreement, with actual severities showing 

low severity and the simulated output showing high severity.

Canopy Base Height (CBH), the average height from the 

ground to the canopy bottom of a forest stand, measured in 

meters, can have a substantial impact on whether a fuel model 

will result in simulated surface-fire behavior (low severity) or 

crown-fire behavior (high severity) in a FlamMap simulation. 

The smaller the CBH, the more likely the area is to produce 

higher, simulated burn intensity. For example, in some areas 

of the Trigo and Big Springs Fires that had burn severities 

of moderate to high according to MTBS, but showed low 

severity (or surface fire) in the FlamMap simulation, the mean 

CBH was 2.1 meters (m), whereas, in other areas of those fires 

where MTBS and the FlamMap simulations were in agreement 

that the severity was high, the mean CBH was 0.7 m.

FlamMap incorporates the VanWagner (1977) crown-fire 

initiation model and the Rothermel (1991) crown fire spread 

model to compute the potential crown-fire activity of an area. 

In an analysis on the sensitivity of VanWagner’s crown-fire 

initiation model, Scott (1998) reported that CBH and the 

surface fuel-model were the critical input factors affecting initiation of crown fires. In 2006, the LANDFIRE team reported 

(U.S. Geological Survey, 2006) that the values in the CBH 

layer were too high to simulate expected crown-fire results 

and recommended modifying this value using a calibration 

factor 0.6106.

For this study, a local calibration and accuracy assessment was completed. Crown-fire activity was modeled using 

CBH calibration factors ranging from 0.1 to 0.6 and compared 

with MTBS burn severity values. The goal of the calibration 

assessment was to find the CBH factor that allowed for the 

closest match between the FlamMap simulated crown-fire 

activity areas and the MTBS moderate and high burn severity areas. The overall accuracy of the FlamMap simulated 

crown-fire activity in predicting MTBS moderate and high 

burn severity with no change to the CBH layer was 57 and 

35 percent for the Trigo and Big Spring Fires, respectively. 

Using a calibration factor of .03 for the CBH layer, the overall accuracy improved to 64 percent for the Trigo Fire and 

62 percent for the Big Spring Fire. Although the accuracy is 

not the highest overall accuracy from the calibration runs, it is 

the point at which there is not a substantial increase in areas 

that MTBS was recorded as having burned at low severity, but 

FlamMap crown-fire activity was predicted to burn with high 

or moderate severity (MTBS low severity equals Crown-Fire 

Activity crown fire). To run the final model, 30-m raster layers 

representing the topography and 2013 fuel conditions were 

obtained from LANDFIRE with the CBH fuel-layer value 

reduced using the revised CBH value of CBH*0.3.

The fuel moisture and weather parameters necessary for 

running the FlamMap model were derived using FireFamily Plus software (U.S. Department of Agriculture, 2002). 

FireFamily Plus is a software system used for summarizing 

and analyzing historical, daily fire-weather observations and 

computing fire-danger indices. Weather parameters used by 

FireFamily Plus include 20-foot above-ground windspeed, 

wind direction, and foliar moisture content. Fuel moisture is 

recorded in terms of live herbaceous fuels, live woody fuels, 

and 1-, 10-, and 100-hour fuels. The 1-hour fuel class represents the finer fuel particles that change in moisture content 

quickly in response to a change in ambient temperature and 

relative humidity.

Weather and fuel moisture data used for the FireFamily 

Plus simulation were from the Oakflats Remote Automated 

Weather Station (RAWS) (Zachariasson and others, 2003), 

which is representative of the Sandia Mountains area. Weather 

and fuel moisture parameters from the Oakflats RAWS used in 

the FireFamily Plus model are shown in table 1–1 and can be 

considered as weather and fuel moisture conditions representative of conditions observed during the Trigo and Big Spring 

Fires. These weather and fuel moisture parameters also were 

used for FlamMap crown-fire activity calibration runs. The 

resulting crown-fire activity layer was used as the burn-severity input in the debris-flow model.

Burn Probability Modeling

Prefire assessment requires an estimation of where 

fires will occur or a burn probability for each location. The 

occurrence and spread of large wildfires (greater than 250 

acres) were modeled to determine burn probability using 

the FSim large fire simulation system (Finney and others, 2011). The focus on “large” wildfires is because these 

Table 1–1. Weather and fuel moisture information at Oakflats 

Remote Automated Weather (RAW) station as reported by Fire 

Family Plus and modified as noted.

[%, percent; mph, miles per hour]

RAW station parameters Input values used

Weather parameters

 Wind directiona Uphill

 Foliar moistureb 80%

 20-foot wind speedc 17 mph

Fuel moisture parameters

 1-hour fuel moisture 5%

 10-hour fuel moisture 8%

 100-hour fuel moisture 12%

 Live herbaceous fuel moisture 30%

 Live woody fuel moistureb 60%

a

Wind direction modeled using uphill wind conditions to represent worst 

case scenario.

b

Expected default data values were used since RAW station output was 

higher than expected for these metrics.

c

Maximum wind speed for April through July.
20 Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico

relatively rare events account for most of the area burned 

(Short, 2013). The FSim simulates thousands of hypothetical 

fire seasons by incorporating fire weather information into 

three primary modules: fire occurrence, fire behavior and 

growth, and fire containment. First, historical fire weather 

information was obtained for the area to be modeled with 

FSim. The National Fire Danger Rating System’s (NFDRS) 

Energy Release Component (ERC) is calculated from this 

weather information for a given fuel model. The ERC is the 

estimated potential available energy released per unit area in 

the flaming zone of a fire and is expressed in British Thermal 

Units per square foot (Bradshaw and others, 1984). The day 

to day variations of the ERC are caused by changes in the 

moisture contents of the various fuel classes (that is, fuel size 

diameters), which are affected by temperature, precipitation, 

and humidity. Fuel models combine the fuel classes into 

a stylized fuel bed for use in fire-behavior modeling. The 

NFDRS fuel models were created specifically for calculating fire danger ratings. The NFDRS fuel model “G” is used 

in the ERC calculations for FSim because it contains all fuel 

classes and is strongly correlated with wildfire occurrence 

across different climate zones (Andrews, 2003). The NFDRS 

fuel model “G” is typified by dense conifer (coniferae) 

stands with heavy accumulation of litter and downed woody 

material. The ERC as it is calculated for fuel model “G” is 

known as ERC(G).

Through the use of a statistical time-series analysis 

weather module (Finney and others, 2011), a series of daily 

fire weather conditions, representing the daily ERC value for 

thousands of possible fire seasons, is generated. Wind speeds 

and directions are independently random for each simulation 

day and follow historic frequency patterns. The fire occurrence module within FSim uses the relation between the 

historical ERCs and historical fire occurrence to determine 

the following: (1) given a daily ERC value, the probability 

that at least one ignition will escape initial suppression efforts 

and become a large fire (that is, probability of escape), and 

(2) given that the probability of escape is met, how many 

ignitions will escape. The escape ignitions are probabilistically located on a landscape for the fire growth simulation 

module, according to historical ignition-density patterns. The 

fire growth module uses FlamMap and the Minimum Travel 

Time (Finney, 2002) logic to grow the fires and determine fire 

intensity based on the fire weather and information regarding 

topography and fuels. The fires increase daily under the wind 

and fuel moistures determined by the weather module until 

the fire is either extinguished because of a number of consecutive days with ERC(G) values below the 80th percentile 

or contained due to suppression (Finney and others, 2011). 

The fire suppression algorithm determines the probability 

of containment due to suppression on any given day, and is 

a function of fire weather, fire duration, and fuel types. The 

FSim calculates annual burn probabilities by dividing the 

number of times a given pixel burns by the number of fire 

seasons simulated.

Burn Probability Model Calibration

Historical wildfire-occurrence data for the FSim modeling extent were obtained from the Fire Program Analysis fireoccurrence database for 1992–2011 (Short, 2013). This information was used to calibrate the FSim model. Over the past 

20 years, the study area contained on average approximately 

4.8 ignitions each year, which resulted in fires larger than 

250 acres. Under the Complete Spatial Randomness hypothesis, the number of events, or points, in a planar region follows 

a Poisson distribution and each point is independent of each 

other (Diggle, 2003). In other words, the points are not clustered or dispersed, they are located randomly about the region. 

An average nearest neighbor spatial statistics test (Ebdon, 

1985) among the large-fire ignitions showed that the large-fire 

ignitions had a less than 1 percent chance of occurring under 

conditions of Complete Spatial Randomness, and showed 

strong evidence of a clustered point densities pattern. These 

results prompted the use of an Ignition Density Grid (IDG), 

which allows the ignitions in FSim to be located randomly on 

the ground in accordance to the density specified by the IDG 

(Finney and others, 2011). An IDG was calculated based on 

the historical fire occurrence data within a 60-kilometer (km) 

buffer of the FSim project areas, using kernel smoothing (Berman and Diggle, 1989), with a kernel width of 50,000 m and a 

quartic kernel function as implemented in spkernel2d function 

(Rowlingson and Diggle, 2013) in R, an open source language 

and a general environment for statistical computing (R Core 

Team, 2012). The IDG and the ignition locations of the recent 

large fires within the FSim project area, and those used in the 

IDG are shown in figure 3 (located at http://pubs.usgs.gov/

sir/2014/5161/downloads/sir2014-5161_fig03.pdf).

The landscape file data layers needed for FSim’s fire 

growth module, FlamMap, were obtained from the LANDFIRE v1.2.0 (fig. 1–2, located at http://pubs.usgs.gov/

sir/2014/5161/downloads/sir2014-5161_fig1_2.pdf) (www.

landfire.gov). The CBH was decreased by 69 percent for the 

timber fuel models, using a calibration factor of 0.316, to 

maintain consistency with the FlamMap inputs as arrived at 

through the FlamMap calibration process (see “Fire-Behavior 

Model Calibration” section). The landscape file inputs were 

resampled from the native resolution of 30 m to 90 m, using 

the nearest neighbor technique (Lillesand and others, 2004, 

p. 750) wherein each cell in the 90-m raster is given the value 

of the nearest 30-m cell (as measured from cell center to cell 

center). The new 90-m resolution is needed for computational 

practicality, and the nearest neighbor technique is used to 

maintain rare and scattered fuel types that other resampling 

techniques would reduce or even eliminate. For example, linear roads and water features, which impede fire growth, would 

be lost using a majority resample.

To generate the relation between historic fire occurrence 

and weather needed by FSim, historical time series data of 

ERC values representative of the FSim project area are needed 

for the time period covered by the national fire occurrence 
Discussion of Hazard Modeling 21

database, 1992–2011 (Short, 2013). The national fire occurrence database was developed to reconcile differences between 

multiple wildfire recording agencies, such as various Federal, 

State, and local entities. The resulting dataset contains more 

than 1.6 million records for the 20-year period, with each 

record spatially located to a precision of at least the Public 

Land Survey Section, and further attributed with the discovery 

date and the final fire size. The weather information commonly 

is extracted from a representative RAWS (Zachariasson and 

others, 2003). The Oakflats (station number 290702) RAWS 

was used in the FlamMap runs for the burn severity model 

calibration; however, this weather station only has information 

dating back to 1993, and there are many missing dates from 

this time period. Other RAWS within the FSim project area 

have the same issue; therefore, the historical gridded ERC dataset (Abataglou, 2011) was used. This dataset is spatially and 

temporally complete from 1979 to 2011, at a 4-km grid for the 

contiguous United States. The necessary 20-year period was 

extracted from this dataset for the 4-km pixel that contained the 

geographic location of the Oakflats RAWS. Percentile weather 

and corresponding ERC streams were formatted for input into 

a FireFamily Plus fire risk file, for use with FlamMap. Winds 

were calculated from the Oakflats RAWS, because only a 

representative of the monthly frequency of wind speeds and 

directions detected in the FSim project area are needed.

A total of 30,000 fire seasons were simulated using FSim, 

with the suppression algorithm enabled, at a resolution of 90 

m. The rate of spread was decreased for the grass fuel models GR1 and GR2, by a factor of 0.4, and for the shrub fuel 

models SH1 and SH2 by a factor of 0.2. The rates of spread 

for these fuel models tend to be extremely fast, and can lead to 

an unrealistically high frequency of extremely large fires in the 

grass and shrub fuel models. Because these fuel models tend 

to be relatively easier to suppress than timber fuel models, and 

tend to occur closer to human development reducing response 

times, a decrease in the rate of spread allows the FSim to suppress these fires in a more realistic time period. A large fire 

cutoff of 400,000 acres was used, which is almost three times 

the size of the largest fire observed in the area in the recent 

past. This large cutoff size allows for fires to burn in the area 

that, to date, are unprecedented in terms of size.

Continuous Parameterization of Burn Probability 

Results

The results of the burn probability modeling were averaged along drainage networks using the continuous parameterization technique (Verdin and Greelee, 2003; Verdin and Worstell, 2008) to create a basin average burn probability index. 

Using this technique, burn probability values were obtained 

for every 10-m pixel along the drainage network as an average 

of burn-probability values in the drainage basin upstream from 

that pixel. This technique allowed for a synoptic view of conditions throughout the entire study area, which could be used 

to identify specific subbasins that might pose a higher risk of 

experiencing a fire. The technique also aided in combining the 

burn-probability hazard with the debris-flow hazards for an 

integrated debris-flow hazard index for each subbasin.

A comparison of modeled burn probability with basinaverage burn probability indices for an area around the Sandia 

Mountains is shown in figure 1–3. The modeled burn probability map shows that the probability of burning an entire basin 

in a single season is unlikely; however, downstream impacts 

to a watershed can be substantial when only the upper parts of 

a watershed are burned, particularly if those areas are severely 

burned. Although the basin-average burn probability values do 

not quantify the likelihood of an entire basin burning in 1 year, 

the values provide a measure of burn hazard that is useful for 

prioritizing hazards by basins.

Discussion of Hazard Modeling

This study was initiated because of a need to ensure that 

widespread watershed restoration efforts taken to safeguard 

vital watersheds against the threat of catastrophic wildfire are 

carried out in places that could have the best return on investment. The study uses a methodology that combines three models in a manner that is not ideal but is necessary for addressing 

a prefire hazard assessment of postfire hazards. The debrisflow model was developed using burn severity but there is 

no model that directly outputs an estimation of burn severity; 

therefore, fire behavior was used as simulated by FlamMap as 

a proxy for burn severity.

Three different sized grid cells were used in the different 

models throughout this study. All gridded inputs for the debrisflow models were in 10-m cell sizes with the exception of the 

FlamMap crown-fire potential. The gridded LANDFIRE fuels 

data and FlamMap crown-fire outputs are in 30-m cell sizes. 

The FlamMap output was resampled to a 10-m cell size using 

the continuous parameterization method. There is no loss of 

information going from a larger (30 m) to a smaller (10 m) cell 

size. Because of the nature of probabilistic fire spread models, 

the landscape size needed to appropriately run FSIM is larger 

than those commonly modeled in a debris-flow assessment; 

therefore, FSIM was run over a large landscape, using a 90-m 

cell size to allow for computation efficiency. The landscape 

file inputs for FSIM were resampled from the native resolution of 30 m to 90 m, using the nearest neighbor methods. 

Although the nearest neighbor methods maintain rare fuel 

types, there is still some loss in data resolution when resampling from a smaller to a larger grid size.

This study has employed applications of several different models beyond what they were originally designed to do. 

The debris-flow models used in this report were designed for 

postwildfire assessments; for this study, they were applied 

before any fire occurred. The FlamMap model was designed 

to model fire behavior; for this study, it was used to estimate 
Tijeras Placitas Sandia Park TIJERAS ARROYO I 40 25 25 BERNALILLO COUNTY SANDOVAL COUNTY

106°30' 106°20'

35°20'35°10'

Tije

Placitas

TIJERAS ARROYO

25

BERNALILLO COU

SANDOVAL COUNT

106°30' 

35°20'35°10'

Universal Transverse Mercator, Zone 13 NorthNorth American Datum of 1983

Universal Transverse Mercator, Zone 13 NorthNorth American Datum of 1983

EXPLANATION

Annual burn probability

[Annual probability of a pixel 

experiencing a wildfire as modeled by 

the large fire simulation system (FSim)]

0 to 0.0010.0011 to 0.0020.0021 to 0.0030.0031 to 0.0040.0041 to 0.005

[

pr

the la

0 0 

0 2.5 5 MILES

0 2.5 5 KILOMETERS

Figure 1–3. Comparison of unadjusted burn probability to basin-average burn probability indices.
References Cited 23

burn severity. The FSIM model was developed to present 

spatial specific burn probabilities; for this study, it was averaged over basin areas and combined with postfire hazards. 

These extensions of the original model applications were 

used because of the need for prioritization of prefire hazards. 

Individually, these models are difficult to verify and the errors 

associated with combining them in the manner used here 

will be difficult to verify as well. Although this methodology 

represents a step forward in how pre-event assessments of 

potential hazards are evaluated, the methodology is still in the 

early stages of development and will continue to evolve as 

more studies like this are completed.

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Publishing support provided by: 

Lafayette and Rolla Publishing Service Centers 

For additional information concerning this publication, contact: 

Director, USGS New Mexico Water Science Center 

5338 Montgomery Blvd., NE 

Albuquerque, NM 87109–1311 

(505) 830–7900

Or visit the New Mexico Water Science Center Web site at: 

http://nm.water.usgs.gov
Back photograph. Hairy Golden Asters (Heterotheca villosa) bloom in the Manzano Mountain Wilderness against a background 

including a portion of the Trigo Fire burn scar. Photograph by Peter Voshefski.
Tillery and others—Prewildfire Evaluation of Postwildfire Debris-Flow Hazards for the Sandia and Manzano Mountains, Central New Mexico—SIR 2014–5161

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20145161