cn 6 th14_aveiro_modelling_runoff_and_erosion_in_a_fire-prone_environment_coelho

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


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


Location of study areas in Portugal

Land degradation after fires in the Caratão

catchment study area


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


• 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


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



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


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


Cumulative 50-year erosion with and without

wildfires


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)


Conceptual model of post-fire response

Example 10-year time series with and without random fires


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


Vertical scales approximately the same. Red dots indicate timing and area of fires


Variability due to weather and random incidence of wildfires

Range with fires

Range without

fires

Interval between managed fires and average biomass & erosion


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

Components of PESERA model


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

cn 6 th14_aveiro_modelling_runoff_and_erosion_in_a_fire-prone_environment_coelho

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