Connecting plot scale erosion experiments with...

Connecting plot scale erosion experiments with small-catchment measurements –

sources of uncertainties

István SISÁKUnversity of Pannonia, Georgikon Faculty, Keszthely, Hungary

talajtan@georgikon.hu

(Soil) Erosion Modelling Workshop20-22 March JRC, Ispra, Italy

Frequency of the tributary monitoringDaily: 3 locationsWeekly: 2 locationsBiweekly: 24 locationsMonthly: 27 locations+ 3 monitored small-catchments

Water quality monitoring in the watershed of Lake Balaton

Land use and other data on the three study catchments

Nagyhorváti Tagyon SomogybabodTotal area of the catchment km2 0.77 0.61 7.03North extent of the catchment º ′ ″ 46º42′48″N 46º54′19″N 46º40′44″NSouth extent of the catchment º ′ ″ 46º41′38″Ν 46º53′22″N 46º38′53″N West extent of the catchment º ′ ″ 17º4′43″E 17º39′41″E 17º43′42″EEast extent of the catchment º ′ ″ 17º5′44″E 17º40′40″E 17º46′15″E

Highest altitude within the catchment m 181 338.8 271Lowest altitude at the outlet m 129.5 157.3 155Average slope % 4.7 12.8 9.9

Land use Forest % 10.5 37.4 56.8Wood at stream and gullies % - - 3.2Arable land % 89.5 - 35.2Small plot viney. a. orch. % - 37.7 -Large field viney. a. orch. % - 27.9 4.8

Cipoletti weir Cipoletti weir Parshall flume

Plot-scale experiments

Sites pHdw pHCaCl2 OM % CaCO3 % sand % silt % clay %

Nagyhorváti(1) 7.3 6.8 1.71 0.4 35.6 41.6 22.8

Somogybabod(2) 8.3 7.7 1.52 22 31.4 51.7 16.9

Tagyon(3) 7.6 7.2 1.81 4.3 41.3 29.5 29.2

Nikla(4) 5.8 5.4 1.20 0 82.1 13.7 4.2

Sites Soil texture classes

Nikla (19 %) slightly silty sand Somogybabod (13 %) sandy loamy silt Nagyhorváti (7 %) slightly sandy loam Tagyon (6 %) sandy clay loam

*(Bodenkundliche Kartieranleitung)

Soil erodibility (USLE-K)* Slaking grade*

Methodology developed in the DESPRAL project2x5 m plot size, 3-4 replicatesSeedbed condition in July-AugustGentle pre-wetting to field capacity60 mm/h simulated rainfall until equilibrium runoff rate reached3-4 consecutive simulations on the same plot with 2-3 day return time

1*3*0.5 m; 25 cm arable soil + 25 cm sand (subsoil)Uniform slope: 13 % (= SB field slope)+ field slope (TA 6%, NH 7%)

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0 0.2 0.4 0.6 0.8 1 1.2

NH-lab 13 %

NH-lab 13 % avg(X;Y)

SB-lab 13 %

SB-lab 13 % avg(X;Y)

TA-lab 13 %

TA-lab 13 % avg(X;Y)

GLM model is suitable to describe the core of the runoff-erosion relationships

y = -3.8495x + 115.38R² = 0.8957

y = -0.4178x + 15.47R² = 0.8927

y = -1.0596x + 34.473R² = 0.6543

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0 5 10 15 20 25 30 35

equi

libriu

m e

rosi

on ra

te g

/m2/

min

equilibrium water conductivity at 60 mm/h simulated rainfall mm/h

Water conductivity and erosion rate in lab and plot measurements

Somogybabod

Tagyon

Nagyhorváti

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regr on ln(runoff) variance by 4 sites variance by 20 plots GLM (COV on ln(runoff)VAR by 4 sites)

GLM (COV on ln(runoff)VAR by 20 plots)

expl

aine

d fr

actio

n of

tota

l var

ianc

e o

f ln(

eros

ion)

Explained variances in rainfall simulation experiments

lg(E) vs. lg(Q)

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0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5lg(Q)

lg(E

)

SBNHTASB: 1.92*X - 2.73; R2=0.94NH: 1.03*X + 0.38; R2=0.50TA: 1.99*X - 1.37; R2=0.82

Event based relationships between runoff an erosion for 3 small catchements

Erosion Modelling Workshop, 20-22 March JRC, Ispra, Italy END OF PRESENTATION

Conclusions:

GLM analyses of lnQ – lnE relationsips provide reliable framework to integrate different scales

Lump estimates of average effects (USLE approach) is better than sole runoff based calculations

Hybrid models are needed: lump estimate of average effects +probability exploration of local variability

Thank you for your attention