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| Titel |
Statistical analysis and modelling of surface runoff from arable fields in central Europe |
| VerfasserIn |
P. Fiener, K. Auerswald, F. Winter, M. Disse |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 10 ; Nr. 17, no. 10 (2013-10-23), S.4121-4132 |
| Datensatznummer |
250085968
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| Publikation (Nr.) |
 copernicus.org/hess-17-4121-2013.pdf |
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| Zusammenfassung |
| Surface runoff generation on arable fields is an important driver of
flooding, on-site and off-site damages by erosion, and of nutrient and
agrochemical transport. In general, three different processes generate
surface runoff (Hortonian runoff, saturation excess runoff, and return of
subsurface flow). Despite the developments in our understanding of these
processes it remains difficult to predict which processes govern runoff
generation during the course of an event or throughout the year, when soil
and vegetation on arable land are passing many states. We analysed the
results from 317 rainfall simulations on 209 soils from different landscapes
with a resolution of 14 286 runoff measurements to determine temporal and
spatial differences in variables governing surface runoff, and to derive and
test a statistical model of surface runoff generation independent from an a priori
selection of modelled process types. Measured runoff was related to 20
time-invariant soil properties, three variable soil properties, four rain
properties, three land use properties and many derived variables describing
interactions and curvilinear behaviour. In an iterative multiple regression
procedure, six of these properties/variables best described initial
abstraction and the hydrograph. To estimate initial abstraction, the
percentages of stone cover above 10% and of sand content in the bulk soil
were needed, while the hydrograph could be predicted best from rain depth
exceeding initial abstraction, rainfall intensity, soil organic carbon
content, and time since last tillage. Combining the multiple regressions to
estimate initial abstraction and surface runoff allowed modelling of
event-specific hydrographs without an a priori assumption of the underlying process.
The statistical model described the measured data well and performed equally
well during validation. In both cases, the model explained 71 and 58%
of variability in accumulated runoff volume and instantaneous runoff rate
(RSME: 5.2 mm and 0.23 mm min−1, respectively), while RMSE of runoff
volume predicted by the curve number model was 50% higher (7.7 mm). Stone
cover, if it exceeded 10%, was most important for the initial abstraction,
while time since tillage was most important for the hydrograph. Time since
tillage is not taken into account either in typical lumped hydrological models
(e.g. SCS curve number approach) or in more mechanistic models using Horton, Green
and Ampt, or Philip type approaches to address infiltration although tillage
affects many physical and biological soil properties that subsequently and
gradually change again. This finding should foster a discussion regarding
our ability to predict surface runoff from arable land, which seemed to be
dominated by agricultural operations that introduce man-made seasonality in
soil hydraulic properties. |
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