Climate change and predicting soil loss from rainfall Peter I. A. Kinnell University of Canberra EGU2017

Modelling Soil Loss in future climates Global Climate Change Model Site specific climate Plot/Hillslope Scale Erosion Catchment Erosion USLE/RUSLE AGNPS RUSLE2 WEPP GEOWEPP EUROSEM LISEM MUSLE/SWAT EROSION 2D EROSION 3D and so on Downscale

Modelling Soil Loss in future climates Global Climate Change Model Site specific climate Examine how well 3 models currently account for event soil losses from bare fallow areas in places with different climates in the USA USLE/RUSLE The most widely used erosion model in the world RUSLE2 Currently used by NRCS as a land management tool WEPP Will take over from RUSLE2’s use by NRCS Models that perform badly in these circumstances are UNLIKELY to perform well in modelling soil loss when climate changes in the future Downscale

USLE/RUSLE Works mathematically in two steps A1 = R K where A1 is the long term soil loss on the unit plot - bare fallow area 22.1 m long on 9% slope with cultivation up and down the slope. R is the rainfall-runoff erosivity factor K is the soil erodibility factor A = A1 L S C P where L, S, C, and P are factors that account for slope lengths, gradients, vegetation and conservation practices that differ from the unit plot. R and K are the factors that account for the geographic variation of climate and soil

USLE/RUSLE R = sum EI30 / years E is total kinetic energy of the raindrops during a storm I30 is the maximum 30-minute rainfall intensity K = total soil loss from unit plot over many years / total EI30 Event soil loss model provides the foundation for the USLE/RUSLE Ae1 = EI30 K As calculated above, K ensures that the total predicted soil loss over the set of events considered equals the total observed soil loss for that set of events

Rainfall erosivity index EI30 USLE/RUSLE Rainfall erosivity index EI30 Event soil loss model : Ae1 = EI30 K Over prediction of low losses Under prediction of high losses Errors distributed randomly about the 1:1 relationship

Soil erodibility factor K USLE/RUSLE Soil erodibility factor K Event soil loss model : Ae1 = EI30 K K = total soil loss from unit plot over many years / total EI30 K from soil properties – the USLE nomograph equation K values were determined from rainfall simulator experiments on bare fallow plots: 3 “storms” (1) applied to a dry surface (2) applied to the plot 24 hours later (3) applied a short time after storm 2 3 different K values : Kdry, Kwet, Kvery.wet K = a Kdry + b Kwet + c Kvery.wet , a + b + c = 1 a, b, c chosen so that K values were appropriate for the climate in the mid west USA Ks determined from the USLE nomograph are for a particular climate in the USA

Soil erodibility factor K USLE/RUSLE Soil erodibility factor K Event soil loss model : Ae1 = EI30 K The revision of the USLE provided a capacity of using soil erodibilities that in effect varied between events Ae1 = EI30 Ke In RUSLE2, the notion that soil erodibilitry varies with the moisture status of the soil is implimented using equations that result in Ke values varying during the year with climate driven variations in rainfall and ambient temperature

RUSLE2 Event soil loss model : Ae1 = EI30 Ke Daily Erodibility The temporal variations in Ke make no appreciable difference to the prediction of event soil loss NSE(ln) Location USLE RUSLE2 Holly Springs,MS 0.528 0.563 Zanesville,OH 0.646 0.644 Tyler,TX 0.342 0.372 Presque Isle, ME 0.177 0.190 Model efficiency

WEPP A more process based model than RUSLE2 Models rill (flow driven) erosion and interrill (raindrop driven) erosion separately Runoff is directly considered in interrill erosivity model Di = ki I q ki = interrill erodibility, I = rainfall intensity, q = runoff rate Rill erosion model includes limiting effect of transport capacity Dr = kr (shear stress – critical shear stress) (1 – sed load / Tc sed load) kr = interrill erodibility Tc sed load = sediment load at transport capacity

WEPP RUSLE2 predicts event soil loss better than WEPP WEPP does not predict runoff and soil loss for all the erosion producing events. WEPP runoff and erosion models calibrated to give total amounts that equaled the total observed runoff and soil loss for the set of events where WEPP predicts soil loss to occur K for RUSLE2 calibrated for same events. Total losses for WEPP and RUSLE2 the same as observed total RUSLE2 predicts event soil loss better than WEPP

Ae1 = QR EI30 KUM , QR = runoff ratio = runoff/rain Runoff and RUSLE2 Event soil loss model : Ae1 = EI30 Ke Does not consider runoff as a factor in causing soil loss Event soil loss = runoff x sediment concentration (loss per unit runoff) Ae1 = Qe [(EI30 / Qe) Ke ] Qe = event runoff Sediment concentration varies with EI30 per unit runoff Analysis of bare fallow runoff and soil loss plots Sediment concentration varies with EI30 per unit RAIN Ae1 = Qe [(EI30 / RAIN) KUM] KUM is the soil erodility associated with using Qe / RAIN x EI30 in place of EI30 Ae1 = QR EI30 KUM , QR = runoff ratio = runoff/rain

Runoff and RUSLE2 Event soil loss model : Ae1 = EI30 Ke Ae1 = QREI30 KUM Ae1 = EI30 (QR KUM) QR KUM can be used as an alterative to Ke in RUSLE2 Largely eliminates the systematic over prediction of small soil losses QR using observed runoff

QR using runoff predicted by WEPP Runoff and RUSLE2 QR using runoff predicted by WEPP NSE(ln) location RUSLE2 USLE-M with WEPP runoff WEPP Bethany, MO 0.325 0.317 -0.258 Holly Springs, MI 0.504 0.605 0.375 Presque Isle, ME 0.101 0.296 -0.115 Watkinsville, GA 0.505 0.362 -0.797 NSE(ln) location RUSLE2 USLE-M with WEPP runoff Bethany, MO 0.325 0.317 Holly Springs, MI 0.504 0.605 Presque Isle, ME 0.101 0.296 Watkinsville, GA 0.505 0.362 Model efficiency Reduced systematic over prediction of small losses but higher random error

Overview Runoff has a major affect on soil loss generated by individual rainfall events In theory, models that take this into account have the best capacity to account for variations in soil loss associated with changes in climate that occur in space (present day) and time (the future) However, inaccurate prediction of the runoff is common and may result in poor performance by models that do take account of the role that runoff has affecting soil loss generated by individual rainfall events. All rainfall erosion models are wrong, some more than others !!!!