www.elsevier.com/locate/advwatres

Advances in Water Resources 30 (2007) 2072–2086

Hydrologic response of upland catchments to wildfires

Maria Cristina Rulli *, Renzo Rosso

Department of Hydraulic, Environmental, Road and Surveying Engineering, Politecnico di Milano, Milan, Italy

Received 24 April 2006; received in revised form 14 September 2006; accepted 15 October 2006Available online 19 April 2007

Abstract

To which extent do wildfires affect runoff production, soil erosion and sediment transport in upland catchments? This transient effectis investigated here by combining data of long term precipitation, sediment yield and wildfire records with a fine resolution spatially dis-tributed modeling approach to flow generation and surface erosion. The model accounts for changes in the structure and properties ofsoil and vegetation cover by combining the tube-flux approach to topographic watershed partition with a parsimonious parametrizationof hydrologic processes. This model is used to predict hydrologic and sediment fluxes for nine small catchments in Saint Gabriel moun-tains of southern California under control (pre-fire) and altered (post-fire) conditions. Simulation runs using a 45 years record of hourlyprecipitation show the passage of fire to significantly modify catchment response to storms with a major effect on erosion and flood flows.The probability of occurrence of major floods in the post-fire season is shown to increase up to an order of magnitude under same pre-cipitation conditions. Also, the expected anomaly of sediment yield can increase dramatically the desertification hazard in upland wildfireprone areas. One should further consider the role of firefloods produced by the combined occurrence of wildfires and storms as a fun-damental source of non-stationarity in the assessment of hydrologic hazard.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Hidrological respose; Firefloods; Sediment yield

1. Introduction

Over the past few years, Mediterranean climate zoneshave had to face a large increase in the number of wildfires;currently there are twice as many as during the 1970s, withan annual accumulated burn area of 600000 ha [1]. Thishas led to a new awareness about the effects of forest fires,not only for the loss of vegetation, but also to the possibleloss of life and property, as well as to changes in the hydro-logical response and erosion/sedimentation process of theonce vegetated areas. Several studies have pointed out theeffects of fire, including reduced infiltration rates, andincreased overland flow [12], accelerated soil erosion[5,6,45], increased soil nutrient losses [40], modificationsin soil physicochemical properties [16], changes in vegeta-tion dynamics [41] and in woody debris motion [33]. Partic-

0309-1708/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.advwatres.2006.10.012

* Corresponding author.E-mail addresses: cristina.rulli@polimi.it (M.C. Rulli), renzo.rosso@

polimi.it (R. Rosso).

ularly, increases in post-fire runoff and erosion rates, by atleast one or two orders of magnitude, as compared withunburned areas, were observed in many Mediterraneancountries [20,34,35,37].

Disappearance of vegetation cover modifies the pro-cesses of interception and evapotranspiration, seriouslyaffecting the hydrological cycle. Forest fires can also affecthydrological processes indirectly, altering the physicalchemical properties of the soil, converting organic groundcover to soluble ash and giving rise to phenomena suchas water repellency (e.g., [11]). Water repellency is anabnormality in soils which results from the coating of soilparticles with organic substances which reduce the affinityshown by the soil for water [13]. Water repellency is animportant factor increasing surface runoff, especially insoils affected by high-intensity fires; these soils present abarrier to water infiltration so that overland flow mayresult. In this context, Campbell et al. [8] described a66% reduction of the infiltration rate in the soil of a pineforest area in Arizona. Other studies indicated that

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2073

physical and hydraulic properties of the soil are scarcelyaffected by fires of light to moderate intensity [18]; in suchcases, time taken for surface runoff to return to pre-fire lev-els will be largely governed by the rate of recovery of veg-etation cover and by the interception characteristics ofvegetation. Imeson et al. [19] pointed out that in soil havingmacropores the infiltration patterns is scarcely influencedby fire. The macropores rapidly conducted water to lowersoil horizons, thus bypassing a water-repellent layer nearthe surface, and preventing high rates of hillslope runofffrom occurring.

Runoff increments have been reported mainly fromMediterranean areas (California, Italy, Tasmania, Israeland the Iberian Peninsula), varying from 11% [3] up to300% [28] in large catchments, to 800% in small basins[8], and up to 50 000% at plot-scale [20]. Flood peak incre-ments range from 45% [3] up to 600% [28] in large catch-ments, to 5700% in small basins [8]. The shape of floodhydrographs are also profoundly altered, with secondarysharp fast peaks replacing smooth recession limbs priorthe fire [7]. Runoff increments are also varying dependingon the time passed from the fire event. Scott and VanWyk [36] showed a 200% increase in the annual runoffand up to 300% for the peak discharges during the year fol-lowing the forest fires in a South African basin. Rulli et al.[35] described an increment ranges from 1800% to 5700% inflood runoff and from 1900% to 3600% of peak dischargesfor about the same rainfall input during events six monthafter the 2003 wildfire in plots located in a italian basin.Lavabre et al. [25] studied the hydrological effects of a for-est fire that burnt 85% of an experimental evergreen-oakMediterranean drainage basin at a different temporal scale.Annual runoff increased 25% for the same annual precipi-tation, related to the reduction in evapotranspiration dueto destruction of vegetation cover, while increase onmonthly and daily runoff reached 30%. Flood frequencywas greatly increased: the 10-year flood estimated beforethe fire occurred three times in the year after the fire,although rainfall events did not exceed the one-year returnperiod. Cerda [9] measured changes in overland flow after awildfire in a typical Mediterranean scrubland during thefollowing 6 years by means of simulated rainfall on plots.Values of the runoff coefficient decreased from the first win-ter (45%) to the sixth winter (6%) after the forest fire. Thereduction of the runoff rates was faster the year after theforest fire, and these changes decreased slightly after thesecond winter.

A review of the literature on sediment yields followingwildfires reveals a highly variable landscape response.Studies in New Mexico and Arizona showed that erosionrates increase dramatically after a wildfire, while erosionis extremely low in stable natural environments [46,31].

Johansen et al. [21] found a sediment production of75 g m�2 h�1 after the Cerro Grande fire (New Mexico)in ponderosa pine forest but a ratio between soil losses inburned and unburned plot of 25; in a Tasmanian eucalyp-tus forest, Wilson [45] observed that post-fire soil erosion

was around 80 g m�2 h�1, but the severely burned areayielded only five times more sediments than the unburnedone; after three different fires, in ponderosa and lodgepolepine forests of the Colorado Front Range, Benavides-Sol-orio and MacDonald [4] measured a sediment productionfrom high-severity burnt areas ranging from 32 to50 g m�2 h�1, resulting in a figure from 10 to 17 times lar-ger than in unburned areas.

Specific studies have been carried out in the Mediterra-nean area to evaluate post-fire erosion and runoff rates andthe recovery period of burned areas. Plot data show anincrease in surface erosion after fire from 2.4 [23] to 423times [14] of the undisturbed ones. In Spanish, areas withclimate and vegetation communities similar to those ana-lysed in the present study, Cerda [9], Marcos et al. [26]and De Luis et al. [14] found that the amount of litterand vegetation cover is a key factor in reducing post-firerunoff and erosion, and in accelerating the recovery timeof burned soils. These results, and those by Inbar et al.[20] and Vega et al. [42] show that the period necessaryfor soil erosion to return to background levels dependson the type of species existing prior to fire; each specieshas its own specific recovery aptitude depending on itsregeneration strategy.

Post-fire recovery analysis in Mediterranean areas showthat soil losses reduce to those of unburnt sites after a per-iod from two [10] to 10 years [20]. These results are consis-tent with those reported in other areas [31]. Also thehighest soil losses per unit area can occur at different timesfrom fire. For example, Soto et al. [38] measured the peakerosion four months after burning, Vega et al. [42]observed the maximum erosion eight months after the fire,and Cerda and Lasanta [10] measured the highest soillosses in two burned plots one year and two year afterburning respectively.

In spite of the relative abundance of plot-scale fieldexperiments, a comprehensive knowledge of fire effectsat the catchment scale is still lacking. Some studiesapproached the impact of fire on basin erosion, upscalingplot-scale data. Hamed et al. [17] used a methodologyadjusting for differences in the kinetic energy of simu-lated and natural rainfall and differences in the slopelengths of the simulated and natural area. In fact, fewphysically based models of fire-driven sediment transporthave been tested and calibrated for predicting sedimentdynamics in natural landscapes [46]. Soto and Diaz [37]used the Water Erosion Prediction Project model formodeling experimental plots predicting runoff and ero-sion. The predicted values were compared with thoseobserved in the field. Wilson et al. [46] provide the firstattempt to model hydrologic processes occurring in largeburned areas (300 km2) in order to predict sedimentlosses after the Cierro Grande fire occurred in the year2000. But hydrologic model of burned areas shouldrequire a deeper insight of underlying physical processesin order to predict the impact of wildfires on catchmentresponse.

2074 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

The present paper provides a physically based approachfor merging observation into model capable of predictingbasin hydrology and erosion after wildfires.

The transient effect of wildfires is investigated here bycombining data of long term precipitation, runoff, sedimentyield and wildfire records with a fine resolution spatiallydistributed modeling approach to flow generation and sur-face erosion. The model accounts for changes in the struc-ture and properties of soil and vegetation cover bycombining the tube-flux approach with topographic

Fig. 1. Location of the study area an

Table 1Features of the investigated catchments in St. Gabriel Mountains, California,

Catchment Area (Km2) DEM size (m) Sl

M

Auburn 0.49 30 1.Bailey 1.55 30 1.Bradbury 1.76 10 1.Englewild 1.13 10 1.Hay 0.51 30 0.Lannan 0.64 30 0.Maddock 0.67 10 1.Morgan 1.55 10 1.Spinks 1.13 10 1.

watershed. Earth surface processes are modeled using aparsimonious parametrization in order to capture the keyfactors controlling basin response. This model is used topredict hydrologic and sediment fluxes for nine smallcatchments in Saint Gabriel mountains of southern Cali-fornia under control (pre-fire) and altered (post-fire) condi-tions. Simulation runs using 45 years record of sub-hourlyprecipitation provide runoff and erosion figures useful toasses the anomalies (i.e., the abrupt deviation or departureof statistics of the investigated variate, e.g., maximum

d of the nine selected catchments.

USA

ope (tana) Average elevation (m)

ax Avg Min a.s.l.

30 0.56 0.07 1,02737 0.65 0.025 78368 0.62 0.035 85544 0.50 0.031 78543 0.22 0.012 30140 0.22 0.029 30632 0.69 0.024 78303 0.47 0.05 91030 0.56 0.07 813

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2075

annual flood or annual sediment yield, from their long termaverages) associated with, these including the flood anderosion increase of potential hazard.

1.1. Methodological assessment

Basin hydrology and erosion are investigated for nineupland catchments under two different soil treatment sce-narios (i.e., burned and not burned). These are modeledusing a spatially distributed approach capable of account-

Lannan

0.E+00

1.E+05

2.E+05

Englewild

0.E+00

5.E+04

1.E+05

Morgan

0.E+00

2.E+04

4.E+04

Bradbury

0.E+00

1.E+05

2.E+05

Hay

0.E+00

1.E+05

2.E+05

(m3/km2)

(100%)

(100%)

(100%)

(100%)

(

(100%)

(100%)(100%)

Auburn

0.E+00

1.E+05

2.E+05

(80%)

Bailey

0.E+00

1.E+05

2.E+05

1950 1960 1970Time

(30%)

Maddock

0.E+00

5.E+04

1.E+05

Spinks

0.E+00

3.E+04

6.E+04

Lannan

0.E+00

1.E+05

2.E+05

Englewild

0.E+00

5.E+04

1.E+05

Morgan

0.E+00

2.E+04

4.E+04

Bradbury

0.E+00

1.E+05

2.E+05

Hay

0.E+00

1.E+05

2.E+05

(m3/km2)

(100%)

(100%)

(100%)

(100%)

(

(100%)

(100%)(100%)

Auburn

0.E+00

1.E+05

2.E+05

(80%)

Bailey

0.E+00

1.E+05

2.E+05

1950 1960 1970Time

(30%)

Maddock

0.E+00

5.E+04

1.E+05

Spinks

0.E+00

3.E+04

6.E+04

Fig. 2. Cumulative sediment yield per unit catchment area from nine catmeasurements and large dots denote the occurrence of wild fires with the perc

ing for those different soil treatment scenarios, and thedetailed spatial coverage of burned areas. The hydrologicmodel provides runoff and sediment fluxes with fine tem-poral resolution in order to capture the impact of stormson earth surface processes. The model is calibrated andvalidated using field data observed in these catchmentsunder the two different scenarios. These data includemaximum annual flows and cumulative sedimentationoccurring in a given temporal interval (i.e., about oneyear).

(100%)

(100%)

(100%)

(100%)

100%)

(85%)

(100%)

1980 1990 2000(Years)

(100%) (100%)

(100%)

(100%)

(100%)

(100%)

100%)

(85%)

(100%)

1980 1990 2000(Years)

(100%)

chments in St. Gabriel mountains, California, USA. Small dots showentage of burned area in parenthesis.

Table 2Model parameters. The values of model parameters insensitive to fireforcing for the investigated catchments in St. Gabriel mountains,California, USA are reported in the table

Parameter Value

Soil conductivity decay parameter (m) 0.3Saturated hydraulic conductivity (m/day) 0.8Soil depth at bedrock (m)Volumetric soil moisture content at saturation (dimensionless) 0.45Volumetric soil moisture content at oven dryness

(dimensionless)0.15

Unsaturated/saturated intercompartment throttling term(dimensionless)

0.7

Bulk specific weight (kg/m3) 1600Sediment specific weight (kg/m3) 2650Median sediment diameter (mm) 0.2Fall velocity of median diameter sediment (m/s) 0.18Manning’s roughness (m�1/3 s)Dry ravel diffusivity (m2/year)Critical hillslope gradientDetachable sediment fraction parameter (mg m�2 s�1)Soil erodibilty factor, (N m)�1

Bare soil fractionVegetated soil fraction

The values of model parameters sensitive to fire are reported in the text.

2076 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

Simulation runs are then performed using log term sub-hourly rainfall records to input to the model under undis-turbed (not burned) and disturbed (burned) scenarios. Fre-quency analysis of simulated data of runoff and erosion arefinally used to asses the impact of fire forcing on maximumannual peak discharge and annual sediment yield underpre-fire and post-fire conditions.

1.2. The study area

The study area is located in the San Gabriel Moun-tains, Southern California (Fig. 1). The San GabrielMountains occupy the central part of the TransverseRanges at the northern margin of the Los Angeles Basinand Pomona Valley. This topographically-steep rangerises, at its maximum, to an elevation of more than3000 m a.s.l. at Mt. San Antonio and it is bordered north-east by the Saint Andrea fault, south by Los Angeles low-lands, northwest by Soledad River Valley and southwestby Saint Fernando Valley. Local climate is mediterraneanand sub-tropical. Major rainfall events occur in wintermonths, from December to March, and rain is very spo-radic in summertime with several uninterrupted drymonths. Mean temperature changes with elevation from5 �C to 20 �C. Most watercourses in the San GabrielMountains are ephemeral streams, dry for most of theyear.

This area displays heavily jointed igneous rocks and, inthe highest range, large granite outcrops. Due to erosionagents and high slope, there is a shallow soil mantle inthe upper areas. Valleys have an alluvial origin and consistof sandy soils with relative high values of permeability,whereas, at the foot of the mountains, the percentage ofclay and silt is higher.

Vegetation cover is mostly constituted by different spe-cies of shrubs and bushes, usually known as chaparral(sclerophyll forest biomes). In the lower area there areoaks, willows and planes; in the higher area, pines andmaples are more common. Besides indigenous plants,eucalyptus is quite widespread. Chaparral and oils con-tained in eucalyptus are highly inflammable and canreach very high temperature during combustion. Thevapors produced by combustion of these organic sub-stances trigger the development of the water repellent soillayer.

Based on the number and extent of wild fires occurred inlast 40 years, nine catchments were selected in the studyarea, named Auburn, Bailey, Bradbury, Englewild, Hay,Lannan, Maddock, Morgan, Spinks, having area rangingfrom 0.49 to 1.76 km2, mean slope (tana, where a is theground inclination angle) from 0.22 to 0.65, maximumslope from 0.40 to 1.68, and with elevation ranging fromabout 300 to about 1000 m a.s.l. (Table 1). Fire data wereretrieved from Los Angeles County Department of PublicWorks (LADPW). These include the time of occurrenceof wild fires, and the percentage of catchment area burnedin each fire episode.

1.3. Debris basin monitoring

Floods in Los Angeles County have been recorded as farback the beginning of the 18th century. For centurieswaters transporting eroded material have swept out ofthe San Gabriel Mountains causing extensive propertydamage and taking a great toll of lives. Each year erodedmaterial in various forms (debris consisting of rock, sandand woody vegetation) flows out of the mountain water-sheds of Los Angeles County. In an effort to control thispotentially disruptive force, the LADPW maintains a seriesof debris basins in canyon mouths and upstream stabiliza-tion structures in selected watersheds. The purpose of adebris basin is to entrap the sediment flows emanating fromthe canyon and let the relatively desilted water pass into thedownstream flood control channels. The Departmentmaintains 115 debris basins and the combined total maxi-mum capacity of the basins is about 6 · 106 m3. Cumula-tive sedimentation is constantly monitored at irregulartime intervals (LADPW, Hydrologic Reports from year1955 to year 1996, Los Angeles, California). Fig. 2 showsthe cumulative sediment yield in the period of observationfor the nine basins. The abrupt step associated with most ofthe marked fire episodes clearly display the influence ofwild fires on sediment yield. But the strength of fire forcingon sediment yield is rather haphazard, because of the com-bined effect of climate. Also, one can detect similar irregu-larities also in the absence of fires.

1.4. The hydrologic model

The model combines process components using awatershed partitioning module. Model structure includes

Fig. 3. Raingauges geographic coordinates (a) and map location (b).

Table 3Gumbel distribution parameters of the Depth Duration Frequency curvesfor the raingauge stations of Mt Wilson, Santa Fe’ and San Gabriel

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2077

a rainfall–runoff module and an erosion and sedimentationmodule. Watershed partitioning is based on the ‘‘streamtube’’ approach [29] as developed by Menduni et al. [27]

Santa Fe' Raingauge - Gumbel Probability Distribution

0

50

100

150

200

250

300

0 10 15 20 25 30

Duration (hours)

Rai

nfal

l Dep

th (m

m)

T=5 anniT=10 anniT=25 anniT=50 anniT=100 anni

San Gabriel Raingauge - Gumbel Probability Distribution

0

50

100

150

200

250

300

350

400

0 10 15 20 25 30Duration (hours)

Rai

nfal

l Dep

th (m

m)

T=5 anniT=10 anniT=25 anniT=50 anniT=100 anni

Mount Wilson Raingauge - Gumbel Probability Distribution

0

50

100

150

200

250

300

350

0 10 15 20 25 30Duration (hours)

Rai

nfal

l Dep

th (m

m)

T=5 anniT=10 anniT=25 anniT=50 anniT=100 anni

5

5

5

Fig. 4. Depth duration frequency curves of rainfall at the three raingaugeslocation.

Raingauge station Gumbel distribution parameters

n (mm) a (mm)

Mt Wilson 0.7737 0.3920Santa Fe 0.6752 0.5627San Gabriel 0.7673 0.4032

i.e., using the drainage path as the skeleton for contour-based watershed modeling. This approach is capable ofproviding an accurate description of terrain morphology,including peaks and saddles, governing surface and subsur-face hydrologic fluxes. The watershed partitioning proce-dure used in the model was developed by Menduni et al.[27], and is based on a preliminary identification of an IdealDrainage Network (IDN) based on contour lines through avariable mesh size, and on a further extraction of theActual Drainage Network (ADN) from the IDN, usingland morphology. The model first derives the steepest slopelines starting from the highest contour and then proceedsdownslope. Once extraction of the ideal drainage networkis complete, the model identifies all the contributing areasdraining to each link (sub-basin). The automatic drainagenetwork delineation is straightforward, and the methodprovides physically consistent one-dimensional streamchannels. Further, the procedure defines polygons boundedby the two steepest adjacent contour lines, orthogonal tothe contour lines. Overland flow is routed through streamtubes, formed by pairs of stream lines constructed normalto the contour lines (equipotentials) of the watershed [29].The model can also automatically identify significant ter-rain characteristics (e.g., peaks and saddles) and accountfor their impact on streamflow. Using this type of discret-ization, the model divides the elements where the waterflows into two classes: cells, that are polygons where theoverland flow is modeled as sheet flow, and channelsobtained by joining two or more stream tubes where sur-face runoff is canalized. This partitioning procedure makesit possible to solve the equations that describe the activehydrological processes in a monodimensional way.

2078 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

Storm flow generation is modeled using the TOPOG-SBM model [43] with the above approach substitutedfor the original TOPOG contour-based watershed parti-tioning. This module consists of (1) a contour-based ele-ment network based on watershed partitioning, (2) asimple bucket model for handling soil water fluxes ineach element (3) a one-dimensional kinematic wave sub-surface flow module for simulating the movement of soilwater downslope along the ‘‘streamtubes,’’ and (4) a onedimensional kinematic wave overland flow module forsimulating surface runoff along the streamtubes. For eachtime step and each catchment element the model com-putes water table depth, soil moisture storage, lateral

Table 4Values of detachable sediment fraction parameter estimated from modelcalibration runs

Detachable sediment fraction parameter mg m�2 s�1

Catchment Scenario

Pre-fire Post-fire

Auburn 0.12 0.75Bailey 0.045 0.16Bradbury 0.15 0.6Englewild 0.15 0.2Hay 0.44 0.9Lannan 0.3 0.88Maddock 0.18 0.85Morgan 0.05 0.3Spinks 0.25 0.9

Fig. 5. Example of partially burned watershed partition using digit

subsurface flow, and overland flow depth, velocity, anddischarge. Three overland flow processes are considered,i.e., (a) infiltration excess, (b) saturation excess, and (c)ex-filtration of upstream sub-surface flow. The choice ofthe SBM model was first driven by the available dataset. Despite its discontinuous nature, the data set of deb-ris production is very rich [24] and, to some extent,unique in this multidisciplinary area. But unfortunatelysome field data necessary for using more sophisticatedmodel of hydrological fluxes are missing (i.e., waterfluxes observations). The SBM is simple, but is capableof capturing the essential feature of the investigatedprocess.

The computed hydrologic fluxes are used to input theerosion and sedimentation module.

This module accounts for both dry and wet ravel. Dryravel, also known as dry creep [22], is the downslope move-ment by gravity of individual grains or aggregates of soil,and it is the most ubiquitous erosional process occurringin the chaparral [30]. Anderson et al. [2] with their 5-yearperiod study of Arroyo Seco, located in the San Gabrielmountains, highlighted as dry-season debris movementexceeded wet season movement of most of sites. Sedimentflux due to dry ravel is modeled using a non-linear trans-port law [32,15] parametrized by diffusivity and criticalhillslope gradient.

The computation of the amount of sediments erodedand deposited by water follows the approach by Wicksand Bathurst [44]. For each hillslope element and at eachtime-step, the amount of sediments produced by the rain-

al elevation models at different resolution (i.e., 30 m and 10 m).

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2079

splash effect and by sheet overland flow is calculated.Raindrops impact breaks the cohesive bonds between soilparticles, thereby detaching particles from the soil massand making them available for transport by water [44].Major factors affecting raindrop detachment are rainfallcharacteristics, soil characteristics, ground and canopycover and surface water depth. These factors are consid-ered individually here, and then combined into a singleequation for predicting soil detachment according to theresults by Styczen and Høgh-Schmidt [39]. This yields toevaluate detachment from the sum of the squares of rain-drop moments.

Eroded sediment are routed downslope by overland flowaccording to the conservation of sediment mass equation.It is solved considering the sediment transport capacity ofoverland flow and the associated potential amount of sed-iments. From the comparison between the amount of sed-

10

100

1,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

Calibration RunsPre-Fire Conditons

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

Bradbury Hay Maddock

Auburn Bailey Englewild

Spinks Lannan Morgan

Calibration RunsPost-Fire Conditons

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

Calibration RunsPre-Fire Conditons

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

Bradbury Hay Maddock

Auburn Bailey Englewild

Spinks Lannan Morgan

Calibration RunsPost-Fire Conditons

Fig. 6. (a–b) Comparison between observed and computed annualsediment yield from model calibration runs (pre- and post-fire conditions).

iments available for transport and the potentials, theamount of sediments actually moved downwards by over-land flow, and the net amount of sediments eroded/depos-ited are calculated. Interrill area detachment caused byraindrops spalsh is computed from rainfall intensity, rain-drop soil erodability coefficient, and proportion of soil cov-ered by ground cover.

Channel erosion is computed as erosion of bed materialand the routing of this material together with that suppliedby overland flow following the same procedure shownbefore for the hillslope element.

Table 2 reports distributed model parameters.

1.5. Hydrologic and Topographic Data

Precipitation data from three raingauges located in thestudy area are used, i.e., Mt. Wilson 2, St. Gabriel and

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

Bradbury

Hay

Maddoc

Aubur

Bailey

Englewild

Spinks

Lannan

Morgan

Validation RunsPre-Fire Conditons

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000Observed (m3)

Com

pute

d (m

3 )

Validation RunsPost-Fire Conditons

10

100

1,000

10,000

100,000

10 100 1,000 10,000 100,000

Observed (m3)

Com

pute

d (m

3 )

k

n

d

s

Validation RunsPre-Fire Conditons

10

100

1,000

10,000

100,000

Observed (m3)

Validation RunsPost-Fire Conditons

Fig. 7. (a–b) Comparison between observed and computed annualsediment yield from model validation runs (pre- and post-fire conditions).

Fig. 8. (a–b) Comparison between observed and computed AFS frommodel validation runs (pre- and post-fire conditions) for Big Bradburycatchment. Big Bradbury (9.03 km2)

0.01

0.1

1

0 10 15 20 25

Peak Discharge (m3/s)

Prob

abili

ty o

f exc

eede

nce

Pre-fire Gumbel Fit to observed data

Observed Data

Pre-fire Gumbel Fit to simulated data

5

Fig. 9. Probability of exceedence of maximum annual peak flow for bigBradbury.

2080 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

St. Fe Dam (Fig. 3). About 45 years of hourly data fromavailable records were retrieved from the National ClimaticData Center. Fig. 4 reports the depth duration frequencycurves for the three raingauges locations. Table 3 reportsthe parameters of Gumbel distribution of ddf. where aand n are, respectively, the scale and position parameters.In addition, annual maximum flows from year 1954 to2002 are available for big Bradbury catchment of9.03 sq km in area; during this period two fires occurred(years 1958 and 1980). Bradbury catchment (1.76 sq km)is a tributary of big Bradbury. These data are useful jointlywith precipitation data in order to validate model runoffpredictions.

In the San Gabriel Mountains area, soil is shallow withan average depth of 0.5–1.0 m. According to the USDAclassification, this soil is classified as loam, consisting of43% sand, 39% silt and 18% clay. Vegetation cover is chap-arral, quite uniformly distributed in the whole study area.The USGS Digital Elevation Model (DEM) provides topo-graphic data with resolution varying from 10 m to 30 m.

For the nine study catchments the available data setconcerning sediments collected at the debris basins con-sisted of 56 measurements in pre-fire condition and 16 inpost-fire condition.

2. Model application

2.1. Model application

From fire histories for the nine selected basins one canidentify three different temporal scenarios in terms ofwatershed response to fire forcing. These are

• The pre-fire scenario, i.e., the period of time before theforest fire occurred;

• the post-fire scenario, i.e., the period from the time whenthe fire extinguished to the end of the spring in the sub-sequent year;

• the transient scenario, i.e., the period of time requiredfor the restoration of the soil, following fire induceddisturbance.

Only the pre-fire and post-fire scenarios are examinedhere.

The first step in model application is watershed partitionresulting in the tubes flux arrangement. Topographic datafrom USGS Digital Elevation Model were used for basinpartitioning procedure. Hourly precipitation data wereused to input the model. Only some of the model parame-ters listed in Table 2 are sensitive to fire forcing. During afire, the organic compounds in the litter and vegetation

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2081

vaporize, and move downward into the soil until they reacha lower temperature soil layer, where condensation occursyielding the formation of a water repellent layer [13]. Rainpercolation in the soil is strongly reduced. The key factor tomodel hydrologic response of a catchment passing frompre-fire to post-fire conditions is given by soil properties.Because of the lacking of hydraulic conductivity measure-ments under post-fire conditions, we assumed soil depthcurrently of 0.5–1.0 m, to be reduced up to 0.05–0.1 munder post-fire conditions. This can mimic the presenceof the hydrophobic layer. The other soil parameters werekept unchanged, and they are reported in Table 2.

AUBURN

0.01

0.1

1

0 1 2 3 4 5

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

BRADBURY

0.01

0.1

1

0 1 2 3 4 5 6

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

HAY

0.01

0.1

1

0 1 2 3 4 5

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

Fig. 10. Probability of exceedence of maximum annual peak flow from the ninGumbel probability distribution fitted to the simulated data.

The effect of fire on erosion is described by the variationof soil erodability factor and the removable sediments frac-tion. Raindrop erodibility factor (Kr) is introduced to rep-resent rainfall impacts on the soil. The literature dataprovide typical values of Kr for different soil types, butone must adjust Kr depending on soil conditions, such asmoisture content and other forcings (e.g., compaction bygrazing cattle, human effects such as tillage, and vegetationeffects such as root binding). Therefore, we regarded Kr asa calibration parameter. Because of uniform soil character-istics in the investigated catchments, a common value ofkr = 35 (Nm)�1 was taken for the unburned scenario,

BAILEY

0.01

0.1

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

ENGLEWILD

0.01

0.1

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

LANNAN

0.01

0.1

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

e catchments under pre-fire and post-fire conditions. Solid lines display the

MADDOCK

0.01

0.1

1

0 1 2 3 4 5

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

MORGAN

0.01

0.1

1

0 1 2 3 4 5 6

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

SPINKS

0.01

0.1

1

0 1 2 3 4 5

Normalized Peak Discharge

Prob

abili

ty o

f exc

eede

nce

Pre-Fire

Post-Fire

Fig. 10 (continued)

2082 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

and kr = 85 (Nm)�1 for post-fire scenario.These are usedfor the nine investigated catchments having uniform soilcharacteristics.

The fraction of detachable sediment depends on theindividual catchment under exam, ranging from 0.045 to0.44 (mg m�2 s�1) for the control scenario and from 0.16to 0.88 (mg m�2 s�1) for the post-fire conditions (Table 4).

Other model parameters sensitive to fire forcing areoverland flow Manning’s roughness (0.2 in hillslopes and0.05 m�1/3 s in channels under pre-fire conditions.) theseare taken to be 0.05 in hillslopes and 0.03 m�1/3 s in chan-nels for post-fire conditions. The bare soil fraction variesfrom 0.1 (pre-fire) to 0.9 (post-fire); the vegetated soil frac-tion from 0.8 to 0; the average canopy height from onemeter to 0; the dry ravel parameters diffusivity and criticalhillslope gradient, respectively, of 27.9 ± 19.2 cm2/year and3.56 in pre-fire conditions and 39.6 ± 30.4 cm2/year and2.67 for burned area [34].

The spatially distributed approach allows to describebasin response of partially burned watersheds (Fig. 5).For partially burned basins the appropriate parameter val-ues are taken for burned and unburned areas.

Model calibration was performed using a single pre-fireseason year data set and a single post-fire season data set.The model equations are integrated using fine temporal res-olution (1 min) using 30 min rainfall as input. Sedimentflux is then properly aggregated in time in order to compare

simulation results to the available cumulative sedimentyield measurements. We assumed that post-fire conditionsoccur in the season year immediately following the extin-tion of the fire. Figs. 6a and 6b show the predicted annualsediment yield for the calibration season years under pre-fire and post-fire conditions, respectively. These runs wereperformed to get the optimal values of the calibrationparameter for the two scenarios. The objective function isto minimize the square deviation from the observed cumu-lated sediment yield.

Model validation runs were further performed using theremaining data set. The resulting sediment yield is com-pared with observations in Fig. 7a and 7b. It is seen thatthe model provides quite accurate sediment yield esti-mated under pre-fire conditions. Validation under post-fireconditions is obviously affected by the scarcity of availabledata.

Model validation of the rainfall – runoff component wascarried out using the big Bradbury data set. Simulationruns were performed using the Bradbury soil parameterset for this 9.03 sq km catchment, and the maximumannual peak flow was extracted from the simulated hydro-graph. The results are compared with observations in Figs.8a and 8b for burned and unburned conditions, respec-tively. In order to discriminate between pre-fire and post-fire conditions, we assumed a recovery period of two yearsafter the post-fire season year.

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2083

2.2. Long term Simulation

Long-term simulation runs were performed using 45years of sub-hourly rainfall records to input the modelof the nine investigated catchments. Individual runs werecarried out to predict catchment response under pre andpost-fire scenarios using the values of model parametersreported in Table 2. The series of Maximum AnnualPeak Flow (hence after referred to as AFS) wereextracted from the continuous discharge series. Longterm simulation of surface runoff was also carried outfor Big Bradbury in order to assess model capability inreproducing extreme values. The results are shown in

AUBURN

0.01

0.1

1

0 20000 40000 60000 80000 100000 120000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

BRADBURY

0.01

0.1

1

0 10000 20000 30000 40000 50000 60000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

HAY

0.01

0.1

1

0 2000 4000 6000 8000 10000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

Fig. 11. Probability of exceedence of annual sediment yield from the nine cGumbel probability distribution fitted to the simulated data.

Fig. 9, where AFS series are fitted using the Gumbeldistribution.

Simulated Sediment yield data were aggregated at theannual scale (hence after referred to as CAS).

Simulation runs for the nine investigated catchmentsprovide 45-years AFS and CAS series under pre-fire andpost-fire conditions, respectively. Frequency analysis wasperformed using the Gumbel distribution as shown inFig. 10 and in Fig. 11, respectively. One notes thatextreme values are slightly underestimated in some cases,so one can use a more sensitive extreme value model tobetter reproduce low frequency values, e.g., the General-ized Extreme Value Distribution. The Gumbel distribution

BAILEY

0.01

0.1

1

0 20000 40000 60000 80000 100000 120000 140000 160000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

ENGLEWILD

0.01

0.1

1

0 50000 100000 150000 200000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

LANNAN

0.01

0.1

1

0 5000 10000 15000 20000 25000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

atchments under pre-fire and post-fire conditions. Solid lines display the

MADDOCK

0.01

0.1

1

0 50000 100000 150000 200000 250000 300000 350000 400000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

MORGAN

0.01

0.1

1

0 10000 20000 30000 40000 50000 60000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

SPINKS

0.01

0.1

1

0 10000 20000 30000 40000 50000 60000 70000 80000

Annual Sediment Yield (m3)

Prob

abili

ty o

f exc

eede

nce Pre-Fire

Post-Fire

Fig. 11 (continued)

Table 5First and second order Statistics of Maximum Annual Peak flow and Annual Sediment Yield from the investigated catchments in St. Gabriel mountains,California, USA, under pre-fire and post-fire conditions

Catchment I and II order statistics maximum annual peak flow I and II order statistics annual sediment yield

Pre-fire Post-fire Pre-fire Post-fire

Mean (m3/s) CV Mean (m3/s) CV Mean (m3) CV Mean (m3) CV

Auburn 0.86 0.52 1.76 0.35 1118 0.83 38859 0.52Bailey 3.60 0.53 5.51 0.43 5347 0.70 51583 0.66Bradbury 3.18 0.63 6.38 0.50 3065 0.82 20577 0.53Englewild 2.70 0.55 4.92 0.30 3108 0.76 57329 0.57Hay 0.60 0.55 1.09 0.37 355 0.86 3180 0.57Lannan 1.34 0.49 2.21 0.40 625 0.65 7831 0.50Maddock 1.22 0.55 2.38 0.39 3843 0.97 91838 0.90Morgan 2.31 0.56 5.74 0.41 993 0.85 20510 0.52Spinks 1.31 0.56 2.49 0.39 2207 0.81 24326 0.59

2084 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

is used here because parameters estimates only depend onsecond order statistics. Both AFS and CAS under fireforcing are dramatically shifted, but the extreme valuesare less sensitive to the transient effect associated with wildfires then the expected values. Table 5 reports the resultsfor the first and second order statistics of maximumannual peak flood and annual sediment yield. The result-ing anomalies of annual sediment yield and annual maxi-mum flood are summarized in Table 6. Table 6 also showsthe percentage anomalies of the 10% quantile estimatedfor Gumbel distribution. The transient fire forcing yields

anomalies of the expected annual sediment yield rangingfrom 6 times to 30 times the expected control scenario sed-iment yield. One also notes that the anomaly of the 10% isslightly lower, ranging from 4 to 27 times the controlvalue. It results that fire forcing tends to be slightlyreduced if extreme storms occur in the post-fireconditions.

The anomalies of flood flows are less dramatic thanthose found for sediment yield, but one notes that the 10-year return period flood can increase up to 100% afterthe passage of fire.

Table 6Expected value and 10% quantile percentage anomalies for MaximumAnnual Flow and for Annual Sediment Yield from the investigatedcatchments in St. Gabriel mountains, California, USA, under pre-fire andpost-fire conditions

Catchment Expected value anomaly 10% Quantile percentageanomaly

Maximumannualpeak flow(%)

Annualsedimentyield (%)

Maximumannualpeak flow(%)

Annualsedimentyield (%)

Auburn 105 3377 76 2743Bailey 53 865 40 806Bradbury 100 571 79 457Englewild 82 1744 45 1550Hay 83 796 57 657Lannan 65 1153 51 1010Maddock 95 2289 67 2257Morgan 149 1965 118 1605Spinks 91 1002 62 850

M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086 2085

This indicates that the combined effect of climate andwild fires can strongly affect erosion in burned areas, sotriggering potential desertification in the absence of promptmitigation measures, and a substantial increase of floodhazard in the transient post-fire conditions.

3. Conclusion

The passage of fire can substantially modify the hydro-logic response of small catchments with a major effect onerosion. Also, flood hazard has a substantial increase, soone can expect that a severe storm can produce flood flowsmuch higher then those expected under stationary control(not burned) conditions. This is shown by combiningobservations with fine resolution hydrological modelingfor nine small catchments in the area of the Saint GabrielMountains (California, USA) under normal (pre-fire) andaltered (post-fire) conditions.

The effect of the forest fire has been investigated by com-bining data of long term precipitation, sediment yield andwildfire records with a fine resolution spatially distributedmodeling approach to flow generation and surface erosion.The model accounts for changes in the structure and prop-erties of soil and vegetation cover by combining the tube-flux approach to topographic watershed partition with aparsimonious parameterization of hydrologic processes.This is because physical (measurable) variables and param-eters are needed to capture the mechanism of soil response,runoff production and sediment detachment. Field dataused for model calibration include annual sediment yieldat the catchment’s scale and maximum annual flood series.Further model validation indicates that a physically basedapproach can help in investigating the sensitivity of catch-ment response to fire forcing.

Simulation runs using a 45 years record of sub-hourlyprecipitation show that the passage of fire strongly changescatchment response to storm precipitation with a major

effect on erosion and flood flows. The probability of occur-rence of major floods in the post-fire season is shown toincrease up to an order of magnitude. Also, the expectedanomaly of sediment yield can dramatically enhance thedesertification hazard in upland wildfire prone areas. Oneshould further consider the role of firefloods produced bythe combined occurrence of wildfires and storms as a fun-damental source of non-stationarity in the assessment ofhydrologic hazard.

Acknowledgements

The authors gratefully thank the anonymous reviewerswho substantially improved the manuscript. This workwas supported by European Union through UE grantIRASMOS-018412.

References

[1] Alexandrian D, Esnault F, Calabri G. Forest fires in the Mediterra-nean area. Unasylva 1999;50:197.

[2] Anderson HW, Coleman GB, Zinke PJ. Summer slides and winterscour: Dry-wet erosion in southern California mountains. US ForServ Res Pap PSW 1959:36.

[3] Anderson, HW, Hoover MD, Reinhart KG. Forests and water:effects of forest management on floods, sedimentation, and watersupply. Gen Tech Rep PSW-18. Berkeley, CA: US Department ofAgriculture, Forest Service, Pacific Southwest Forest and RangeExperiment Station; 1976.

[4] Benavides-Solorio J, MacDonald LH. Post-fire runoff and erosionfrom simulated rainfall on small plot, Colorado Front Range. HydrolProcess 2001;15:2931–52.

[5] Booker FA , Landscape and management response to wildfires inCalifornia. MS thesis, University of California at Berkeley, Berkeley;1998.

[6] Booker FA, Dietrich WE. Landscape and management response towildfires in California, California Department of Forestry and FireProtection, Burned Watershed Erosion Study, CDF No. 8CA38629;1998.

[7] Brown JAH. Hydrologic effects of a bushfire in a catchment in south-eastern new South Wales. J Hydrol 1972;15:77–96.

[8] Campbell RE, Baker MB, Folliott PF. Wildfire effects on a ponderosapine ecosystem: an Arizona case study. USDA Forest Service Papers,RM-191. Rocky Mountain Forest and Range Experimental Station,Fort Collins, Colorado; 1977.

[9] Cerda A. Changes in overland flow and infiltration after a rangelandfire in Mediterranean scrubland. Hydrol Process 1998;12:1031–42.

[10] Cerda A, Lasanta T. Long-term erosional responses after fire in theCentral Spanish Pyrenees 1. Water Sediment Yield Catena2005;60:59–80.

[11] Chandler C, Cheney P, Thomas P, Trabaud L, Williams D. Fire inforestry forest fire behaviour and effects, vol. 1. New York: Wiley;1983. p. 450.

[12] DeBano LF. Water repellent soils: a state of the art. Gen Tech Rep,PSW-46, Pacific Southwest Forest and Range Experiment Station,US Dept Agriculture, Berkeley, CA; 1981.

[13] DeBano LF. The role of fire and soil heating on water repellencyin wildland environments: a review. J Hydrol 2000:195–206. May29.

[14] De Luis M, Gonzalez-Hidalgo JC, Raventos J. Effects of fire andtorrential rainfall on erosion in a Mediterranean gorse community.Land Degrad Dev 2003;14:203–13.

[15] Gabet EJ. Sediment transport by dry ravel. J Geophys Res2003;108(B1):1–22. doi:10.1029/2001JB00168.

2086 M.C. Rulli, R. Rosso / Advances in Water Resources 30 (2007) 2072–2086

[16] Giovannini G, Lucchesi S. Modifications induced in the soil physio-chemical parameters by experimental fires at different intensities. SoilSci 1997;162:479–86.

[17] Hamed Y, Albergel J, Pepin Y, Asseline J, Nasri S, Zante P, et al.Comparison between rainfall simulator erosion and observed reser-voir sedimentation in an erosion-sensitive semiarid catchment. Catena2002;50:1–16.

[18] Hudson J, Kellman M, Sanmugadas K, Alvarado C. Prescribed burn-ing Pinus occarpa in Honduras. Forest Ecol Manage 1983;5:269–81.

[19] Imeson AC, Verstraten JM, van Mulligen EJ, Sevink J. The effects offire and water repellency on infiltration and runoff under Mediter-ranean type forest. Catena 1992;19:345–61.

[20] Inbar M, Tamir M, Wittenberg L. Runoff and erosion processes aftera forest fire in Mount Carmel, a Mediterranean area. Geomorphology1998;24:17–33.

[21] Johansen PJ, Hakonson TE, Breshears DD. Post fire runoff anderosion from rainfall simulation: contrasting forests with shrublandsand grasslands. Hydrol Process 2001;15:2953–65.

[22] Krammes JS. Seasonal debris movement from steep mountainsideslopes in southern California. In: Federal Inter-Agency Sedimenta-tion Conference, Miscellaneous Publication, US Department ofAgriculture, 1965;970;85–8.

[23] Kutiel P, Lavee H, Segev M, Benyamini Y. The effect of fire-inducedsurface heterogeneity on rainfall–runoff–erosion relationships in aneastern Mediterranean ecosystem, Israel. Catena 1995;25:77–87.

[24] Lave J, Burbank D. Denudation processes and rates in the TransverseRanges, southern California: Erosional response of a transitionallandscape to external and anthropogenic forcing. J Geophys Res2004;109. doi:10.1029/2003JF00002.

[25] Lavabre J, Sempere D, Cernesson F. Changes in the hydrologicalresponse of a small Mediterranean basin a year after a wildfire. JHydrol 1993;142:273–99.

[26] Marcos E, Tarrega R, Luis-Calabuig E. Comparative analysis ofrunoff and sediment yield with a rainfall simulator after experimentalfire. Arid Soil Res Rehabil 2000;14:293–307.

[27] Menduni G, Pagani A, Rulli MC, Rosso R. A non conventionalwatershed partitioning method for semidistributed hydrologicalmodelling: the package Aladhyn. Hydrol Process 2002;16:277–91.

[28] Nasseri I. Frequency of floods from a burned chaparral watershed.In: Proceeding of the symposium on fire and watershed management.General Technical Report PSW-109, USDA (Forest Service). Berke-ley, California; 1988.

[29] Onstad CA, Brakensiek DL. Watershed simulation by stream pathanalogy. Water Resour Res 1968;4(5):965–71.

[30] Rice RM. Sedimentation in the chaparral: How do you handleunusual events? In: Swanson FJ, et al., editors. Sediment budgets androuting in forested drainage basins, For. Serv. US Dep. Agric.,Washington, DC; 1982. p. 39–49.

[31] Robichaud PR. Fire effects on infiltration rates prescribed fire inNorthern Rocky Mountain forests, USA. J Hydrol 2000:220–9.

[32] Roering JJ, Kirchner JW, Dietrich WE. Evidence for nonlinear,diffusive sediment transport on hillslopes and implications forlandscape morphology. Water Resour Res 1999;35:853–70.

[33] Rosso R, Rulli MC, Bocchiola D. Transient catchmenthydrology after wildfires in a Mediterranean basin: runoff, sedimentand woody debris. Hydrol and Earth Syst Sci 2007;11(1): 125–40.

[34] Rulli MC, Rosso R. Modeling catchment erosion after wildfires in theSan Gabriel Mountains of southern California, Geophys. Res Lett2005;32:L19401. doi:10.1029/2005GL02363.

[35] Rulli MC, Bozzi S, Spada M, Bocchiola D, Rosso R. Rainfallsimulations on a fire disturbed Mediterranean area. J Hydrol2006;327(3–4):323–38.

[36] Scott DF, Van Wyk DB. The effects of wildfire on soil wettability andhydrological behaviour of an afforested catchment. J Hydrol1990;121:239–56.

[37] Soto B, Diaz-Fierros F. Runoff and soil erosion from areas of burntscrub: Comparison of experimental results with those predicted by theWEPP model. Catena 1998;31:257–70.

[38] Soto B, Basanta R, Perez R, Diaz-Fierros F. An experimental studyof the influence of traditional slash and burn practices on soil erosion.Catena 1995;24:13–23.

[39] Styczen M, Høgh-Schmidt K. A new description of splash erosion inrelation to raindrop sizes and vegetation. In: Rickson, RJ editor.Erosion assessment and modelling, morgan RPC, Commission ofEuropean Communities Report EUR 10860 EN; 1988. p. 147–84.

[40] Thomas AD, Walsh RPD, Shakesby RA. Nutrient losses in erodedsediment after fire in eucalyptus and pine forests in the wetMediterranean environment of northern Portugal. Catena1999;36:283–302.

[41] Trabaud L, Whelan RJ. The ecology of fire Book review. Acta Oecol1996;16(5).

[42] Vega JA, Fernandez C, Fonturbel T. Throughfall runoff and soilerosion after prescribed burning in gorse shrubland in Galicia (NWSpain). Land Degrad Develop 2005;16:37–51.

[43] Vertessy RA, Elsenbeer H. Distributed modeling of storm generationin an Amazonian rain forest catchment: effects of model parameter-ization. Water Resour Res 1999;35(7):2173–87.

[44] Wicks JM, Bathurst JC. SHESED: a physically based, distributederosion and sediment yield component for the SHE hydrologicalmodelling system. J Hydrol 1996;175:213–38.

[45] Wilson CJ. Effects of logging and fire on runoff and erosion on highlyerodible granitic soils in Tasmania. Water Resour Res 1999;35(11):3531–46.

[46] Wilson CJ, Carey JW, Beeson PC, Gard MO, Lane LJ. A GIS-basedhillslope erosion and sediment delivery model and its application inthe Cerro Grande burn area. Hydrol Process 2001;15:3011–23.