 |
| Titel |
The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US |
| VerfasserIn |
J. Oh, T. Sinha, A. Sankarasubramanian |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 8 ; Nr. 18, no. 8 (2014-08-06), S.2885-2898 |
| Datensatznummer |
250120426
|
| Publikation (Nr.) |
 copernicus.org/hess-18-2885-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| It is well known in the hydrometeorology literature that developing real-time
daily streamflow forecasts in a given season significantly depends on the
skill of daily precipitation forecasts over the watershed. 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 daily nutrient loadings and the
associated concentration could be predicted using daily precipitation
forecasts and previously observed streamflow as surrogates of antecedent land
surface conditions. By selecting 18 relatively undeveloped basins in the
southeast US (SEUS), we evaluate the skill in predicting observed total
nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing
the daily streamflow forecasts using the retrospective weather forecasts
based on K-nearest neighbor (K-NN) resampling approach and then forcing the
forecasted streamflow with a nutrient load estimation (LOADEST) model to
obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN
loadings and the associated concentration were computed using rank
correlation and RMSE (root mean square error), by comparing the respective
forecast values with the WQN observations for the selected 18 Hydro-Climatic
Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings
and their concentration have statistically significant skill in predicting
the respective daily observations in the WQN database at all 18 stations over
the SEUS. Only two stations showed statistically insignificant relationships
in predicting the observed nitrogen concentration. We also found that the
skill in predicting the observed TN loadings increases with the increase in
drainage area, which indicates that the large-scale precipitation reforecasts
correlate better with precipitation and streamflow over large watersheds. To
overcome the limited samplings of TN in the WQN data, we extended the
analyses by developing retrospective daily streamflow forecasts over the
period 1979–2012 using reforecasts based on the K-NN resampling approach.
Based on the coefficient of determination (R2Q-daily) of the
daily streamflow forecasts, we computed the potential skill
(R2TN-daily) in developing daily nutrient forecasts based on
the R2 of the LOADEST model for each station. The analyses showed that
the forecasting skills of TN loadings are relatively better in the winter and
spring months, while skills are inferior during summer months. Despite these
limitations, there is potential in utilizing the daily streamflow forecasts
derived from real-time weather forecasts for developing daily nutrient
forecasts, which could be employed for various adaptive nutrient management
strategies for ensuring better water quality. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|