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| Titel |
Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty |
| VerfasserIn |
K. Steffens, M. Larsbo, J. Moeys, E. Kjellström, N. Jarvis, E. Lewan |
| 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 ; 18, no. 2 ; Nr. 18, no. 2 (2014-02-06), S.479-491 |
| Datensatznummer |
250120271
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| Publikation (Nr.) |
 copernicus.org/hess-18-479-2014.pdf |
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| Zusammenfassung |
| Assessing climate change impacts on pesticide
leaching requires careful consideration of different sources of
uncertainty. We investigated the uncertainty related to climate
scenario input and its importance relative to parameter uncertainty
of the pesticide leaching model. The pesticide fate model MACRO was
calibrated against a comprehensive one-year field data set for
a well-structured clay soil in south-western Sweden. We obtained an
ensemble of 56 acceptable parameter sets that represented the
parameter uncertainty. Nine different climate model projections of
the regional climate model RCA3 were available as driven by
different combinations of global climate models (GCM), greenhouse
gas emission scenarios and initial states of the GCM. The future
time series of weather data used to drive the MACRO model were
generated by scaling a reference climate data set (1970–1999) for
an important agricultural production area in south-western Sweden based
on monthly change factors for 2070–2099. 30 yr simulations
were performed for different combinations of pesticide properties
and application seasons. Our analysis showed that both the
magnitude and the direction of predicted change in pesticide
leaching from present to future depended strongly on the particular
climate scenario. The effect of parameter uncertainty was of major
importance for simulating absolute pesticide losses, whereas the
climate uncertainty was relatively more important for predictions of
changes of pesticide losses from present to future. The climate
uncertainty should be accounted for by applying an ensemble of
different climate scenarios. The aggregated ensemble prediction
based on both acceptable parameterizations and different climate
scenarios has the potential to provide robust probabilistic estimates of future
pesticide losses. |
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