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| Titel |
Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States |
| VerfasserIn |
J. Oh, A. Sankarasubramanian |
| 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 ; 16, no. 7 ; Nr. 16, no. 7 (2012-07-24), S.2285-2298 |
| Datensatznummer |
250013379
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| Publikation (Nr.) |
 copernicus.org/hess-16-2285-2012.pdf |
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| Zusammenfassung |
| It is well established in the hydroclimatic literature that the interannual
variability in seasonal streamflow could be partially explained using
climatic precursors such as tropical sea surface temperature (SST)
conditions. Similarly, it is widely known that streamflow is the most
important predictor in estimating nutrient loadings and the associated
concentration. The intent of this study is to bridge these two findings so
that nutrient loadings could be predicted using season-ahead climate
forecasts forced with forecasted SSTs. By selecting 18 relatively
undeveloped basins in the Southeast US (SEUS), we relate winter
(January-February-March, JFM) precipitation forecasts that influence the JFM
streamflow over the basin to develop winter forecasts of nutrient loadings.
For this purpose, we consider two different types of low-dimensional
statistical models to predict 3-month ahead nutrient loadings based on
retrospective climate forecasts. Split sample validation of the predictive
models shows that 18–45% of interannual variability in observed winter
nutrient loadings could be predicted even before the beginning of the season
for at least 8 stations. Stations that have very high coefficient of
determination (> 0.8) in predicting the observed water quality
network (WQN) loadings during JFM exhibit significant skill in predicting
seasonal total nitrogen (TN) loadings using climate forecasts. Incorporating
antecedent flow conditions (December flow) as an additional predictor did
not increase the explained variance in these stations, but substantially
reduced the root-mean-square error (RMSE) in the predicted loadings.
Relating the dominant mode of winter nutrient loadings over 18 stations
clearly illustrates the association with El Niño Southern Oscillation (ENSO)
conditions. Potential utility of these season-ahead nutrient predictions in
developing proactive and adaptive nutrient management strategies is also
discussed. |
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