1. Esteves, T.C.J.1
; Ferreira, A.J.D.1
; Soares, J.A.A.2
; Kirkby,
M.J.3
; Shakesby, R.A.4
; Irvine B.J.3
Ferreira, C.S.S.1
; Coelho,
C.O.A.2
, Carreiras, M.A.1
1 Dpt. of Environment, Escola Superior Agrária de Coimbra, Coimbra, 3040-316, Portugal
2 Dpt. of Environment and Planning, Universidade de Aveiro, Aveiro, 3810-193, Portugal
3 School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
4 Dpt. of Geography, Swansea University, SA2 8PP, United Kingdom
Modelling runoff and erosion in a
fire-prone environment
LANDCON October 2010 Mjk: Slide 1
Background
To Portguese study sites
To PESERA model
Application of PESERA to post-fire responses

2. Fires and soil degradation
LANDCON October 2010 Mjk: Slide 2
Dry summer
vegetation
Wild fires
Accidental
Ignition
Increased
Soil Erosion
Re-growth of
Vegetation
Irreversible
soil
degradation
Loss of fine
earth and
nutrients
Seasonal
climate
Positive
Impact
Sustainable
Un-sustainable

3. LANDCON October 2010 Mjk: Slide 3
Location of study areas in Portugal

4. Land degradation after
fires in the Caratão
catchment
study area
LANDCON October 2010 Mjk: Slide 4
Former forests of Pinus Pinaster and
Eucalyptus globulus.
70% burned 1998-2005
Steep (>20o
) stony cambisols over
metamorphic rocks

5. Experimental fire in Vale
Torto catchment on February 20th
2009
View of the catchment near Góis, 4
months after the prescribed fire
LANDCON October 2010 Mjk: Slide 5
• Catchments monitored
before & 2 years after fire
for infiltration, runoff ,
sediment yield and
vegetation
• Control catchment
monitored in parallel over
the same period
• Main measures adopted
were preventive forestry

6. The DES!RE Project
• Look at degradation and conservation in
an integrated way
• Improve indicators of soil degradation
status
• Develop promising mitigation/
remediation methods for each area with
stakeholders
• Evaluate effectiveness of measures locally
• Use models to evaluate potential
effectiveness for a wider surrounding areaLANDCON October 2010 Mjk: Slide 6

7. Preventive forestry conservation measures
LANDCON October 2010 Mjk: Slide 7

8. Biophysical model based on PESERA
(Pan-European Soil Erosion Risk Assessment)
• A previously developed coarse scale model to provide an
estimator of soil erosion risk at the regional scale
• Applicable at 1 km resolution with existing pan-European
data , but OK down to c. 100m with better data from study
sites.
• Explicit physical basis originally designed primarily to
– i) monitor regional distribution of erosion risk and
– ii) examine future risk under climate/ land use scenarios.
• Potential to apply observed rainfall and compare with
observed erosion rates for calibration/ validation
• Continued support through current EU projects (DES!RE,
DESURVEY, MIRAGE)
• Potential to provide outputs for biomass, Soil organic
Matter, moisture status and water quality
LANDCON October 2010 Mjk: Slide 8

9. LANDCON October 2010 Mjk: Slide 9
Gridded (50 km) Climate Data or
Rf, Temp & Pot E-T
Vegetation
Biomass
(kg/m2
)
Runoff and Climate/Vegetation
Erosion Potential, Ω
Combined Erosion, kΛΩ
DigitalSoil,land-useandGeology
mapsat1:500000
Topographic Potential, Λ
DTM (50-250m grid)
Erodibility, k
Runoff
Water
balance
(SMD)
Soil
Storage
Ground Cover:
Compare with AVHRR
Partitioning of
hydrology
ET

10. Main PESERA Input data sources at 1 km
resolution
Parameter Default
Source
for
Europe
Grid Res’n
Climate Daily rainfall
Potential E-T, Temp
MARS 50km
Soil Texture, crusting,
erodibility, water storage
capacity, Effective depth
(m)
European
Soil
Database
1km
Land use Category of use, crop,
planting dates, rooting
depth, initial cover, water
use efficiency
CORINE
2000
250m
1km
Topography Standard deviation of
elevation around each
SRTM 90m
LANDCON October 2010 Mjk: Slide 10

11. Legend
estimated annual erosion
(t/ha/yr)
0
0 - 0.5
0.500000000 - 1
1.000000001 - 3
3.000000001 - 5
5.000000001 - 10
10.00000001 - 30
30.00000001 - 50
Primary output from PESERA model
LANDCON October 2010 Mjk: Slide 11

12. Modifications to PESERA to model fire
response• Fire Ignition & Spread
– Fire Danger Index (FDI) calculated from Temperature, Temp.
Range and number of dry days in each month
– Number of fire start-ups estimated from visitor numbers and
frequency of lightning strikes (generally unimportant in
Europe)
– Probability of fire = No of Start-ups x FDI
– Area & Intensity of fire increases with wind speed and
decreases with fuel load (biomass) and its moisture content.
• Post-fire erosion and recovery
– Partial destruction of Biomass, Cover and Soil Organic Matter
in response to severity of burn, increasing post-fire erosion rate
– Some delay in erosion onset as highly absorbent ash layer wets
up
– Additional Increase in post-fire erodibility due to more
disturbed available material. This component reduces in
proportion to subsequent rainfall amounts.
– Regrowth of vegetation and cover (using existing routines)
associated with further reduction in erosionLANDCON October 2010 Mjk: Slide 12

13. Fire probability and occurrence
in an example 50 year period
LANDCON October 2010 Mjk: Slide 13

14. Cumulative 50-year
erosion with and without
wildfires
LANDCON October 2010 Mjk: Slide 14
With wildfires:
Fires shown in red
(Value indicates fire
area)
Without wildfires
Largest non-fire erosion
event (240 mm in month:
49 mm in day)
Erosion event increased
following major fire (210
mm in month: 25 mm in a
day)

15. LANDCON October 2010 Mjk: Slide 15
Conceptual model of post-fire response

16. Example 10-year time series with and without random
fires
LANDCON October 2010 Mjk: Slide 16
Largest erosion event
when heavy rainfall
coincides with a
moderate-sized fire
With wildfires –fires are black
spikes
No wildfires –same climatic
sequence
Largest fire damages
vegetation – takes 5 years to
recover
Largest non-fire erosion
event - impact almost
unchanged

17. Four realisations of 50 -yr wildfire
regime. Climate is the same, and only
random fire occurrence changes
LANDCON October 2010 Mjk: Slide 17
Vertical scales approximately the same.
Red dots indicate timing and area of fires

18. LANDCON October 2010 Mjk: Slide 18
Variability due to
weather and random
incidence of wildfires
Range
with fires
Range
without
fires

19. Interval between managed fires
and average biomass & erosion
LANDCON October 2010 Mjk: Slide 19
Erosion level
with no fires
Biomass level
with no fires
Number of
wildfires
almost
unchanged ,
but less severe
As interval between managed fires decreases (to the left),
average biomass is decreased, erosion is reduced, but wildfire
are as frequent, though smaller in area and less in severity

20. Effect of a 2o
C temperature rise
in this Portugal environment
• Increases potential E-T (50%)
• Increased Winter Actual E-T (15% over year)
• Slight increase in Biomass
• Slight decrease in Soil Organic Matter
• Slight decrease in Soil Erosion without Fires
• 20% Increase in Fire frequency and severity,
but re-growth in winter after fires is more
rapid
• Ratio of erosion with : without fires
increased, but the total rate is not as high as
at present.LANDCON October 2010 Mjk: Slide 20

21. Conclusions
• Modelling is able to simulate at least some of
the major interactions between fire and
erosion
• Most important effects not yet incorporated:
– Thinning of soil and irreversible soil loss
– Hydrophobic increases immediately after fire
• Main effects shown by modelling
– Response to fires is very strongly dependent on
the magnitude of immediately following storms
– Prescribed fires reduce total erosion, but not
necessarily the number of small wildfires
LANDCON October 2010 Mjk: Slide 21

22. LANDCON October 2010 Mjk: Slide 22

23. Components of PESERA model
LANDCON October 2010 Mjk: Slide 23
Land Cover Soil TypeClimate Topography
Storm
Runoff
Threshold
Distribution of
Storm and Non-
storm Runoff
Saturated
Subsurface Flow,
Snowmelt and
Frozen Ground
Runoff
Erodibility
Relief
Accumulated
Erosion