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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, Portugal2 Dpt. of Environment and Planning, Universidade de Aveiro, Aveiro, 3810-193, Portugal
3 School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom4 Dpt. of Geography, Swansea University, SA2 8PP, United Kingdom
Modelling runoff and erosion in a fire-prone environment
LANDCON October 2010 Mjk: Slide 1
BackgroundTo Portguese study sitesTo PESERA model
Application of PESERA to post-fire responses
Fires and soil degradation
LANDCON October 2010 Mjk: Slide 2
Dry summer vegetation
Wild firesAccidental
Ignition
Increased Soil Erosion
Re-growth of Vegetation
Irreversible soil
degradation
Loss of fine earth and nutrients
Seasonal climate
Positive Impact
Negative Impact
Sustainable
Un-sustainable
LANDCON October 2010 Mjk: Slide 3
Location of study areas in Portugal
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
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 (Mação) and prescribed fires (Vale Torto)
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 area• Disseminate results to stakeholdersLANDCON October 2010 Mjk: Slide 6
Preventive forestry conservation measures
LANDCON October 2010 Mjk: Slide 7
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
LANDCON October 2010 Mjk: Slide 9
Gridded (50 km) Climate Data or Rf, Temp & Pot E-T
VegetationBiomass (kg/m2)
Runoff and Climate/Vegetation Erosion Potential, Ω
Combined Erosion, kΛΩ
Dig
ital
Soi
l, la
nd-u
se a
nd G
eolo
gy
map
s at
1:5
00 0
00
Topographic Potential, ΛDTM (50-250m grid)
Erodibility, k
Runoff
Water balance(SMD)
Soil Storage
Ground Cover:Compare with AVHRR
Partitioning of hydrologyET
Main PESERA Input data sources at 1 km resolution
Parameter Default Source for Europe
Grid Res’n
Climate Daily rainfallPotential 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
250m1km
Topography Standard deviation of elevation around each point
SRTM 90mLANDCON October 2010 Mjk: Slide 10
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
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
Fire probability and occurrence in an example 50 year period
LANDCON October 2010 Mjk: Slide 13
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)
LANDCON October 2010 Mjk: Slide 15
Conceptual model of post-fire response
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
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
LANDCON October 2010 Mjk: Slide 18
Variability due to weather and random incidence of wildfires
Range with fires
Range without
fires
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
Effect of a 2oC 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
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 wildfiresLANDCON October 2010 Mjk: Slide 21
LANDCON October 2010 Mjk: Slide 22
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