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Prediction of Short Term Soil Losses P.I.A. Kinnell University of Canberra USLE/RUSLE Soil Loss = f (climate, soil, topography, landuse) A = R K LS C P A = average annual soil loss 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 Water quality concerns make the modelling of soil losses at event and seasonal time scales desirable P.I.A. Kinnell University of Canberra USLE/RUSLE R = average annual sum of event energy (E) and the maximum 30-minute intensity (I30) Event soil loss from bare fallow area: Ae.1 = K (EI30)e Event soil loss Under predicts large losses Over predicts small losses P.I.A. Kinnell University of Canberra Event Soil Loss Prediction Soil loss in terms of runoff and sediment concentration Ae1 = Qe ce Ae1 = K EI30 = Qe (K EI30/Qe) Ae1 = unit plot event loss Qe = event runoff ce = sediment concentration for event 2.5 sed conc (t/ha/mm) Morris Plot 5 y = 0.0009x R2 = 0.3838 ce = f (rainfall intensity , energy per unit quantity of rain) ce a I30 E / rainfall ce1 amount = K [EI30/Qe] Ae1 = k1 Qe (I30 E / rainfall amount) 2 1.5 1 0.5 0 ce1 = k1 [EI30/rain] 0 200 400 600 800 1000 1200 1400 EI30/Q (MJ/ha/h) P.I.A. Kinnell University of Canberra Event Soil Loss Prediction Erosion in terms of runoff and sediment concentration Ae1 = Qe ce Ae1 = unit plot event loss Qe = event runoff ce = sediment concentration for event 3 2.5 ce = f (rainfall intensity , energy per unit quantity of rain) ce a I30 E / rainfall amount Ae1 = k1 Qe (I30 E / rainfall amount) 2.5 2 sed conc (t/ha/mm) sed conc (t/ha/mm) 2 1.5 1 1.5 1 0.5 y = 0.0839x R2 = 0.8642 0.5 0 0 0 5 10 15 20 25 EI30/rain (MJ/ha/mm) Morris Plot 5 y = 0.0009x R2 = 0.3838 Morris Plot 5 ce1 = k1 [EI30/rain] P.I.A. Kinnell 30 35 0 200 400 600 800 1000 1200 EI30/Q (MJ/ha/h) ce1 = K [EI30/Qe] University of Canberra 1400 Event Soil Loss Prediction Event soil loss Ae1 = k1 Q [ EI30/rain] Predicts erosion more accurately Approach used in USLE variant called the USLE-M Event soil loss Ae1 = K EI30 P.I.A. Kinnell University of Canberra Event Soil Loss Prediction Event soil loss Event soil loss USLE - M top 5 Actual (t/ha) 177 Lowest 10 P.I.A. Kinnell 0.83 USLE/RUSLE USLE-M 164 (-7%) 1.12 USLE/RUSLE 123 (-31%) 25 (+2910%) University of Canberra Event Soil Loss Prediction Event erosion Ae1 = k1 Q [ EI30/rain] Predicts erosion more accurately k1 > K because EI30/rain < EI30/Q while Ae1 remains the same Event erosion Ae1 = K EI30 USLE-M soil factor differs from USLE/RUSLE soil factor P.I.A. Kinnell University of Canberra Event Soil Loss Prediction USLE-M Soil Factor (KUM) differs from USLE/RUSLE Soil Factor (KU) Soil Bath Caribou Mexico Monona Honeye Grenada Shelby Barnes P.I.A. Kinnell KUM KU KUM/KU 0.0088 0.0536 0.0728 0.0737 0.0836 0.0933 0.1228 0.1337 0.0031 (1) 0.0162 (2) 0.0327 (4) 0.0262 (3) 0.0390 (6) 0.0667( 8) 0.0619 (7) 0.0345 (5) 2.7 3.3 2.2 2.8 2.1 1.4 2.1 3.8 University of Canberra USLE based on Unit Plot approach Key issue = Unit Plot is 22 m long 9% slope gradient Bare fallow (no vegetation), cultivation up and down slope L = S = C = P = 1.0 A=RK P.I.A. Kinnell University of Canberra Unit Plot 22m long Wheat Plot 9% slope 33m long 6% slope A1 = R K=10 t/ha AC =A1 ( L S C P) AC =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 A1 then AC P.I.A. Kinnell University of Canberra Event Soil Loss Prediction Predicting event erosion on unit plot well DOES NOT help predict erosion on cropped areas 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 vs Observed Observed event event Predicted soil loss from cropped plot erosion on cropped plot Observed = 0 P.I.A. Kinnell X = Event soil losses observed for conventional corn + 0.0001 University of Canberra Event Soil Loss Prediction Predicting event erosion on unit plot well DOES NOT help predict erosion on cropped areas 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 P.I.A. Kinnell Predicted vs Observed event erosion on cropped plot Observed = 0 X = Event soil losses observed for conventional corn + 0.0001 University of Canberra 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 Ae = [QR EI30] KUM L S CUM.e PUM.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 P.I.A. Kinnell University of Canberra Event Soil Loss Prediction CUM: USLE-M C factor (annual values) P.I.A. Kinnell Location Bethany, Missouri Crop alfalfa corn corn/meadow/wheat CUM 0.008 0.674 0.188 CU CUM/CU 0.002 4.0 0.628 1.1 0.106 1.8 Clarinda, Iowa corn corn/oats/meadow 0.634 0.424 0.316 0.168 2.0 2.5 Guthrie, Oklahoma cotton Bermuda grass wheat/clover/cotton 2.435 0.064 0.913 1.357 0.002 0.344 1.8 32.3 2.7 LaCrosse, Wisconsin corn 0.527 0.469 1.1 Madison, S.Dakota corn(ploughed) corn(mulch till) 0.486 0.384 0.337 0.250 1.4 1.5 Morris, Minnesota corn meadow/corn/oats 0.520 0.046 0.434 0.010 1.2 4.6 Presque Isle, Maine potatoes 0.634 0.316 2.0 corn (mean) 0.569 0.437 1.3 Corn University of Canberra Event Soil Loss Prediction CUM: USLE-M C factor (annual values) P.I.A. Kinnell Location Bethany, Missouri Crop alfalfa corn corn/meadow/wheat CUM 0.008 0.674 0.188 CU CUM/CU 0.002 4.0 0.628 1.1 0.106 1.8 Clarinda, Iowa corn corn/oats/meadow 0.634 0.424 0.316 0.168 2.0 2.5 Guthrie, Oklahoma cotton Bermuda grass wheat/clover/cotton 2.435 0.064 0.913 1.357 0.002 0.344 1.8 32.3 2.7 LaCrosse, Wisconsin corn 0.527 0.469 1.1 Madison, S.Dakota corn(ploughed) corn(mulch till) 0.486 0.384 0.337 0.250 1.4 1.5 Morris, Minnesota corn meadow/corn/oats 0.520 0.046 0.434 0.010 1.2 4.6 Presque Isle, Maine potatoes 0.634 0.316 2.0 corn (mean) 0.569 0.437 1.3 Bermuda grass Corn University of Canberra 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. 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 The development of the USLE-M is consistent with that objective P.I.A. Kinnell University of Canberra Event Soil Loss Prediction The Modified Universal Soil Loss Equation The MUSLE - developed by Williams (1975) Replaces EI30 with a(Qe qp)0.56 Qe = event runoff amount qp = peak runoff rate a = empirical coefficient Used in SWAT P.I.A. Kinnell University of Canberra The Modified Universal Soil Loss Equation Uses USLE K, L, S, C, P factors in event approach Ae = [a(Qe qp)0.56 ] X K L S Ce Pe USLE K should NOT be used. KMUSLE should be calculated for the fact that Re is not equal to EI30 K = N Ae.1 e=1 —————— N (EI30)e e=1 P.I.A. Kinnell KMUSLE = N Ae.1 e=1 —————— N (a(Q qp)0.56 )e e=1 University of Canberra The Modified Universal Soil Loss Equation Uses USLE K, L, S, C, P factors in event approach Ae = [a(Qe qp)0.56 ] X K L SX CeX Pe USLE K should NOT be used. KMUSLE should be calculated for the fact that Re is not equal to EI30 C and P are influenced by runoff in the USLE. They too need to be calculated for for the fact that Re is not equal to EI30 P.I.A. Kinnell University of Canberra The Modified Universal Soil Loss Equation Ae = [a(Qe qp)0.56 ] K L S Ce Pe INVALID variant of the USLE/RUSLE model P.I.A. Kinnell University of Canberra Agricultural pollution model 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 P.I.A. Kinnell University of Canberra