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 (Qin) 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 × 0.5 m of cell size) to simulate time to ponding (Tp), runoff duration (TQ), initial runoff per raster cell (q0), Qsim and Qin in six olive plots (480 m2 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 (Qobs) in gauging-stations during 27 time-integrated samples and used for modelling validation (162 control points). On average, Qobs 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 Qsim appeared in another 51 and 52 events in the plots with CC and CT. Tp with CT was 2.3 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 q0 with CC were 2.3% lower than with CT and total Qsim with CC was 2% higher than with CT. However, the differences of Qsim 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%. Qsim correlated well with Qobs (Pearson ca. 0.861), and Qsim was overestimated ca. 10%. The model performed better when rainfall depth and intensity were high, and the range of variability of both Qsim and Qobs 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 q0, Qobs, Qsim and actual available water (Waa, 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.

Bibliographic Details

Main Authors:	López-Vicente, Manuel, García-Ruiz, Roberto, Guzmán, Gema, Vicente-Vicente, J. L., Van Wesemael, Bas, Gómez Calero, José Alfonso
Other Authors:	Universidad de Jaén
Format:	artículo biblioteca
Language:	English
Published:	Elsevier 2016-12
Subjects:	Runoff yield, Runon, Olive orchard, Cover crops, Conventional tillage, DR2 model,
Online Access:	http://hdl.handle.net/10261/150595 http://dx.doi.org/10.13039/501100007064 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100004336 http://dx.doi.org/10.13039/501100011011
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