Temporal stability and patterns of runoff and runon with different cover crops in an olive orchard (SW Andalusia, Spain)

Conventional tillage (CT) and cover crops (CC) trigger different runoff (Q) and runon (Q(in)) magnitudes and patterns in woody crops. The spatial and temporal stability of these patterns is not well known yet. In this study, we run the uncalibrated DR2-2013 SAGA v1.1 model (0.5 x 0.5 m of cell size) to simulate time to ponding (Tp), runoff duration (T-Q), initial runoff per raster cell (q(0)), and Qin in six olive plots (480 m(2) per plot) during two years (108 rainfall events and 648 simulations). Two plots were managed with a mixture of plant species (CC-I), two with one single plant species (CC-II) and two with CT. Runoff yield from each plot was collected (Q(obs)) in gauging-stations during 27 time-integrated samples and used for modelling validation (162 control points). On average, Q(obs) was 9% higher under CT than under CC-I, and 8% higher than under CC-II. Topsoil saturation was simulated for the entire plots during 29 events (test-period), and Q(sim), appeared in another 51 and 52 events in the plots with CC and CT. Tp with CT was 23 times higher (59 s) than the average duration with CC and the topsoil became saturated 3.3 times faster in the inter-rows than below the trees. Values of go with CC were 23% lower than with CT and total Q(sim), with CC was 2% higher than with CT. However, the differences of Qq, between the different treatments were not statistically significant. The mean observed and simulated runoff coefficients were of 11 and 14%, with median values of 7 and 10%. (2.5or, correlated well with Q(obs) (Pearson ca. 0.861), and Qs,m was overestimated ca. 10%. The model performed better when rainfall depth and intensity were high, and the range of variability of both Q(sim) and Q(obs) , was similar. The average, best and worst Nash Sutcliffe coefficients were 0.665, 0.791 (P6) and 0.512 (P3) and thus model simulations were satisfactory. The four plots with CC presented on average a worse performance (Kling Gupta coefficient = 0.607) than the two plots with CT (KGE = 0.769). The lowest spatial variability of qo, Q(obs) , Q(sim) and actual available water (Waci, the sum of Qin and stored water in the soil surface) were found in the plots with CC. CT triggered higher spatial variability of runoff and higher temporal variability of runon than CC. (C) 2016 Published by Elsevier B.V.