World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Interannual Hydroclimatic Variability and Its Influence on Winter Nutrients Variability Over the Southeast United States : Volume 8, Issue 6 (12/12/2011)

By Oh, J.

Click here to view

Book Id: WPLBN0004012788
Format Type: PDF Article :
File Size: Pages 37
Reproduction Date: 2015

Title: Interannual Hydroclimatic Variability and Its Influence on Winter Nutrients Variability Over the Southeast United States : Volume 8, Issue 6 (12/12/2011)  
Author: Oh, J.
Volume: Vol. 8, Issue 6
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Sankarasubramanian, A., & Oh, J. (2011). Interannual Hydroclimatic Variability and Its Influence on Winter Nutrients Variability Over the Southeast United States : Volume 8, Issue 6 (12/12/2011). Retrieved from

Description: Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-5908, USA. 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 R2(LOADEST) (>0.8) in predicting the observed WQN loadings during the winter (Table 2) exhibit significant skill in loadings. Incorporating antecedent flow conditions (December flow) as an additional predictor did not increase the explained variance in these stations, but substantially reduced the 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.

Interannual hydroclimatic variability and its influence on winter nutrients variability over the southeast United States

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974.; Alexander, R. B. and Smith, R. A.: Trends in the nutrient enrichment of U.S. rivers during the late 20th century and their relation to changes in probable stream trophic conditions, Limnol. Oceanogr., 51, 639–654, 2006.; Alexander, R. B., Slack, J. R., Ludtke, A. S., Fitzgerald, K. K., and Schertz, T. L.: Data from selected US Geological Survey national stream water quality monitoring networks, Water Resour. Res., 34, 2401–2405, 1998.; Arhonditsis, G. B., Winder, M., Brett, M. T., and Schindler, D. E.: Patterns and mechanisms of phytoplankton variability in Lake Washington (USA), Water Res., 38, 4013–4027, 2004.; Borsuk, M. E., Stow, C. A., and Reckhow, K. H.: Confounding effect of flow on estuarine response to nitrogen loading, J. Environ. Eng., 130, 605–614, 2004.; Chen, C. F., Ma, H. W., and Reckhow, K. H.: Assessment of water quality management with a systematic qualitative uncertainty analysis, Sci. Total Environ., 374, 13–25, 2007.; Childers, D. L., Boyer, J. N., Davis, S. E., Madden, C. J., Rudnick, D. T., and Sklar, F. H.: Relating precipitation and water management to nutrient concentrations in the oligotrophic upside-down estuaries of the Florida Everglades, Limnol. Oceanogr., 51, 602–616, 2006.; Cho, H. J. and Poirrier, M. A.: Response of submersed aquatic vegetation (SAV) in Lake Pontchartrain, Louisiana to the 1997–2001 El Nino Southern Oscillation shifts, Estuaries, 28, 215–225, 2005.; Cohn, T. A.: Estimating contaminant loads in rivers: An application of adjusted maximum likelihood to type 1 censored data, Water Resour. Res., 41, W07003, doi:10.1029/2004WR003833, 2005.; Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D., and Summers, R. M.: The Validity of a Simple Statistical-Model for Estimating Fluvial Constituent Loads – an Empirical-Study Involving Nutrient Loads Entering Chesapeake Bay, Water Resour. Res., 28, 2353–2363, 1992.; Dettinger, M. D. and Diaz, H. F.: Global characteristics of stream flow seasonality and variability, J. Hydrometeorol., 1, 289–310, 2000.; Devineni, N. and Sankarasubramanian, A.: Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs, Geophys. Res. Lett., 37, L24704, doi:10.1029/2010GL044989, 2010.; Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W09404, doi:10.1029/2006WR005855, 2008.; Douglas, E. M., Vogel, R. M., and Kroll, C. N.: Trends in Flood and Low Flows in the United States, J. Hydrol., 240, 90–105, 2000.; EPA Total Maximum Daily Load Report: available at:, 2006.; Hartigan, J. A. and Wong M. A.: Algorithm AS 136: A k-means clustering algorithm, Applied Statistics, 28.1, 100–108, 1979.; Kaplan, A., Cane, M., Kushnir, Y., Clement, A., Blumenthal, M., and Rajagopalan, B.: Analyses of global sea surface temperature 1856–1991, J. Geophys. Res., 103, 18567–18589, 1998.; Landman, W. A. and Goddard, L.: Model Output Statistics Applied to Multi-Model Ensemble Forecasts for Southern Africa, Proceedings of the Seventh International Conference on Southern Hemisphere Meteorology and Oceanography, 249–250, 2003.; Leung, L. R., Hamlet, A. F., Lettenmaier, D. P., and Kumar, A.: Simulations of the ENSO Hydroclimate Signals in the Pacific Northwest Columbia River Basin, B. Am. Meteorol. Soc., 80, 2313–2329, 1999.; Li, S. and Goddard, L.: Retrospective Forecasts with the ECHAM4.5 AGCM, IRI Technical Report, 05–02, 2005.; Meybeck, M.: Carbon, Nitrogen, and Phosphorus Transport by World Rivers, Am. J. Sci., 282, 401–450, 1982.; Muell


Click To View

Additional Books

  • Applying a Time-lapse Camera Network to ... (by )
  • Cloud Obstruction and Snow Cover in Alpi... (by )
  • Series Distance – an Intuitive Metric to... (by )
  • Catchment Conceptualisation for Examinin... (by )
  • Comparison Between Field Measurements an... (by )
  • A Global Water Cycle Reanalysis (2003–20... (by )
  • Evolutionary Geomorphology: Thresholds a... (by )
  • Climate Resources Analysis for Use of Pl... (by )
  • Land Use Change Effects on Runoff Genera... (by )
  • Estimating Spatial Mean Root-zone Soil M... (by )
  • Influence of Initial Heterogeneities and... (by )
  • Prediction of Dissolved Reactive Phospho... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.