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Prediction of Short Term Soil Losses P.I.A. KinnellUniversity of Canberra.

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Presentation on theme: "Prediction of Short Term Soil Losses P.I.A. KinnellUniversity of Canberra."— Presentation transcript:

1 Prediction of Short Term Soil Losses P.I.A. KinnellUniversity of Canberra

2   Designed for looking at long term average annual erosion in field sized areas Not for predicting soil losses by individual events or the year by year variation in soil loss Soil Loss = f (climate, soil, topography, landuse) A = R K LS C P A = average annual soil loss USLE/RUSLE Water quality concerns make the modelling of soil losses at event and seasonal time scales desirable P.I.A. KinnellUniversity of Canberra

3 R = average annual sum of event energy (E) and the maximum 30-minute intensity (I 30 ) Event soil loss from bare fallow area: A e.1 = K (EI 30 ) e  Under predicts large losses  Over predicts small losses Event soil loss USLE/RUSLE P.I.A. KinnellUniversity of Canberra

4 Event Soil Loss Prediction A e1 = Q e c e A e1 = unit plot event loss Q e = event runoff c e = sediment concentration for event A e1 = Q e c e A e1 = unit plot event loss Q e = event runoff c e = sediment concentration for event A e1 = K EI 30 = Q e (K EI 30 /Q e ) A e1 = K EI 30 = Q e (K EI 30 /Q e ) c e = f (rainfall intensity, energy per unit quantity of rain) c e = f (rainfall intensity, energy per unit quantity of rain) c e  I 30 E / rainfall amount c e  I 30 E / rainfall amount A e1 = k 1 Q e (I 30 E / rainfall amount) A e1 = k 1 Q e (I 30 E / rainfall amount) c e1 = k 1 [EI 30 /rain] c e1 = k 1 [EI 30 /rain] Soil loss in terms of runoff and sediment concentration c e1 = K [EI 30 /Q e ] P.I.A. KinnellUniversity of Canberra

5 Event Soil Loss Prediction A e1 = Q e c e A e1 = unit plot event loss Q e = event runoff c e = sediment concentration for event A e1 = Q e c e A e1 = unit plot event loss Q e = event runoff c e = sediment concentration for event c e = f (rainfall intensity, energy per unit quantity of rain) c e = f (rainfall intensity, energy per unit quantity of rain) c e  I 30 E / rainfall amount c e  I 30 E / rainfall amount A e1 = k 1 Q e (I 30 E / rainfall amount) A e1 = k 1 Q e (I 30 E / rainfall amount) c e1 = k 1 [EI 30 /rain] c e1 = K [EI 30 /Q e ] c e1 = k 1 [EI 30 /rain] c e1 = K [EI 30 /Q e ] Erosion in terms of runoff and sediment concentration P.I.A. KinnellUniversity of Canberra

6 Event Soil Loss Prediction  A e1 = k 1 Q [ EI 30/ rain] A e1 = K EI 30  Approach used in USLE variant called the USLE-M Event soil loss Predicts erosion more accurately P.I.A. KinnellUniversity of Canberra

7 Event Soil Loss Prediction Event soil loss Actual (t/ha) USLE-M USLE/RUSLE  top (-7%) 123 (-31%)  Lowest (+2910%) USLE - M USLE/RUSLE P.I.A. KinnellUniversity of Canberra

8 Event Soil Loss Prediction  A e1 = k 1 Q [ EI 30 /rain] A e1 = K EI 30  Event erosion Predicts erosion more accurately k 1 > K because EI 30 /rain K because EI 30 /rain < EI 30 /Q while A e1 remains the same USLE-M soil factor differs from USLE/RUSLE soil factor P.I.A. KinnellUniversity of Canberra

9 Event Soil Loss Prediction USLE-M Soil Factor (K UM ) differs from USLE/RUSLE Soil Factor (K U ) USLE-M Soil Factor (K UM ) differs from USLE/RUSLE Soil Factor (K U ) P.I.A. KinnellUniversity of Canberra

10 USLE based on Unit Plot approach Key issue = Unit Plot is 22 m long 22 m long 9% slope gradient 9% slope gradient Bare fallow (no vegetation), cultivation up and down slope Bare fallow (no vegetation), cultivation up and down slope L = S = C = P = 1.0 A = R K L = S = C = P = 1.0 A = R K P.I.A. KinnellUniversity of Canberra

11 A 1 = R K=10 t/ha A C =A 1 ( L S C P ) A C =10 (1.22 x 0.57 x 0.16 x 1.0) = 1.1 t/ha L, S, C and P are all ratios with respect to unit plot conditions The model operates in two stages - predicts A 1 then A C Unit Plot 22m long 9% slope Wheat Plot 33m long 6% slope P.I.A. KinnellUniversity of Canberra

12 Event Soil Loss Prediction Y = Event soil loss for conventional corn predicted by multiplying event soil losses from a nearby bare fallow plot by fortnightly C factor values Predicted vs Observed event soil loss from cropped plot X = Event soil losses observed for conventional corn P.I.A. KinnellUniversity of Canberra Predicting event erosion on unit plot well DOES NOT help predict erosion on cropped areas Predicted vs Observed event erosion on cropped plot Observed = 0

13 Event Soil Loss Prediction Y = Event erosion for conventional corn predicted by multiplying event soil losses from a nearby bare fallow plot by fortnightly C factor values Erosion predicted when none occurs X = Event soil losses observed for conventional corn Observed = 0 Predicted vs Observed event erosion on cropped plot Predicting event erosion on unit plot well DOES NOT help predict erosion on cropped areas P.I.A. KinnellUniversity of Canberra

14 Event Soil Loss Prediction To predict event erosion from vegetated area must use runoff from vegetated area in calculation of event erosivity index for the USLE-M A e = [Q R EI 30 ] K UM L S C UM.e P UM.e To predict event erosion from vegetated area must use runoff from vegetated area in calculation of event erosivity index for the USLE-M A e = [Q R EI 30 ] K UM L S C UM.e P UM.e Thus, values for crop and conservation factors for USLE-M differ from those used in the USLE because erosivity index is not directed at predicting soil loss from unit plot Thus, values for crop and conservation factors for USLE-M differ from those used in the USLE because erosivity index is not directed at predicting soil loss from unit plot P.I.A. KinnellUniversity of Canberra

15 Event Soil Loss Prediction C UM : USLE-M C factor (annual values) C UM : USLE-M C factor (annual values) Corn P.I.A. KinnellUniversity of Canberra

16 Event Soil Loss Prediction Bermuda grass C UM : USLE-M C factor (annual values) C UM : USLE-M C factor (annual values) Corn P.I.A. KinnellUniversity of Canberra

17 Event Soil Loss Prediction Soil erodibility prediction K UM values from plot studies - K UM values from soil properties (as per RUSLE) Soil erodibility prediction K UM values from plot studies - K UM values from soil properties (as per RUSLE) Crop factor prediction C UM values from plot studies - short term C UM values from plant properties (as per RUSLE ) Crop factor prediction C UM values from plot studies - short term C UM values from plant properties (as per RUSLE ) Research issues associated with the USLE-M: P.I.A. KinnellUniversity of Canberra

18 Event Soil Loss Prediction With the need to consider short term (daily) sediment loads in rivers, NPS pollution models need to predict erosion on a daily basis. With the need to consider short term (daily) sediment loads in rivers, NPS pollution models need to predict erosion on a daily basis. In some models, long term average annual erosion values are disaggregated to give daily erosion In some models, long term average annual erosion values are disaggregated to give daily erosion Alternatively, modelling daily erosion directly is seen as the better approach Alternatively, modelling daily erosion directly is seen as the better approach The development of the USLE-M is consistent with that objective The development of the USLE-M is consistent with that objective P.I.A. KinnellUniversity of Canberra

19 Event Soil Loss Prediction The Modified Universal Soil Loss Equation The MUSLE - developed by Williams (1975) The Modified Universal Soil Loss Equation The MUSLE - developed by Williams (1975) Replaces EI 30 with  (Q e q p ) 0.56 Q e = event runoff amount q p = peak runoff rate  = empirical coefficient Replaces EI 30 with  (Q e q p ) 0.56 Q e = event runoff amount q p = peak runoff rate  = empirical coefficient Used in SWAT Used in SWAT P.I.A. KinnellUniversity of Canberra

20 The Modified Universal Soil Loss Equation Uses USLE K, L, S, C, P factors in event approach A e = [  (Q e q p ) 0.56 ] K L S C e P e Uses USLE K, L, S, C, P factors in event approach A e = [  (Q e q p ) 0.56 ] K L S C e P e USLE K should NOT be used. K MUSLE should be calculated for the fact that R e is not equal to EI 30 USLE K should NOT be used. K MUSLE should be calculated for the fact that R e is not equal to EI 30 NN  A e.1  A e.1 e=1e=1 K = —————— K MUSLE = —————— N N  (EI 30 ) e  (  (Q q p ) 0.56 ) e e=1e=1 N N  A e.1  A e.1 e=1 e=1 K = —————— K MUSLE = —————— N N  (EI 30 ) e  (  (Q q p ) 0.56 ) e e=1 e=1 X P.I.A. KinnellUniversity of Canberra

21 The Modified Universal Soil Loss Equation Uses USLE K, L, S, C, P factors in event approach A e = [  (Q e q p ) 0.56 ] K L S C e P e Uses USLE K, L, S, C, P factors in event approach A e = [  (Q e q p ) 0.56 ] K L S C e P e USLE K should NOT be used. K MUSLE should be calculated for the fact that R e is not equal to EI 30 USLE K should NOT be used. K MUSLE should be calculated for the fact that R e is not equal to EI 30 C and P are influenced by runoff in the USLE. They too need to be calculated for for the fact that R e is not equal to EI 30 C and P are influenced by runoff in the USLE. They too need to be calculated for for the fact that R e is not equal to EI 30 X XX P.I.A. KinnellUniversity of Canberra

22 The Modified Universal Soil Loss Equation A e = [  (Q e q p ) 0.56 ] K L S C e P e A e = [  (Q e q p ) 0.56 ] K L S C e P eINVALID variant of the USLE/RUSLE model P.I.A. KinnellUniversity of Canberra

23 Scientific shortcomings in the modelling of short term erosion in catchments Need to overcome them in order to develop more scientifically robust aids to making decisions on land management Agricultural pollution model P.I.A. KinnellUniversity of Canberra


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