Environmental Monitoring and Assessment

, Volume 185, Issue 2, pp 1939–1949 | Cite as

Temporal scale-induced uncertainty in load duration curves for instream-dissolved oxygen

  • Abhijit Patil
  • Zhiqiang Deng
  • Ronald F. Malone


Load duration curves were developed using the Hydrological Simulation Program FORTRAN (HSPF) for dissolved oxygen (DO) for the Amite River in Louisiana, USA. The concept of ‘dissolved oxygen reserve’, defined as the total quantity of DO, is introduced. The effect of temporal resolution on duration curves of DO reserve was examined using duration curves developed based on daily, weekly, biweekly, and monthly average data. Duration curves for DO exhibited high variability in the load estimated using daily data as compared to those based on biweekly and monthly data. A seasonal analysis revealed the trend in the DO reserve. The daily DO reserve for the Amite River at Port Vincent was 44,049.31 kg when daily summer data were used and 74,255.15 kg for daily annual data. A surplus of 10,691 kg of DO reserve was shown in the monthly data during critical summer months. The coefficient of variation (CV), used to define the temporal scale-induced uncertainty, was found to be linearly and inversely correlated with the logarithm of the time scale. Regression equations were developed to extrapolate near real-time flow and water quality data, greatly simplifying flow and water quality monitoring and reducing the cost involved in flow and water quality monitoring.


Dissolved oxygen Load duration curves Total maximum daily load Temporal scale Uncertainty 



Support for this research by the National Aeronautics and Space Administration (NASA) and the USGS/Louisiana Water Resources Research Institute is gratefully acknowledged.


  1. Cleland, B. (2002). TMDL development from the “bottom up”—Part II: Using duration curves to connect the pieces. National TMDL Science and Policy 2002—WEF Specialty Conference, America’s Clean Water Foundation. Phoenix, AZ, USA.Google Scholar
  2. Cleland, B. (2008). Back to basics—Using hydrology to connect TMDLs and storm water management programs. 16th National Nonpoint Source Monitoring Workshop, Columbus, OH, USA.Google Scholar
  3. Deng, Z.-Q. and Patil, A. (2011). Assessment of water quality variation in Amite River watershed under changing climate and land use. In: Water quality: Current trends and expected climate change impacts, IAHS Publ. 348, IAHS Press.Google Scholar
  4. DeWalt, R.E. (1995). Biological communities of reference streams in the South-Central plains and Upper Mississippi alluvial plains ecoregions of Louisiana. Louisiana Department of Environmental Quality, Office of Water Resources.Google Scholar
  5. Ice, G. (2003). Summer dissolved oxygen concentrations in forested streams of northern Louisiana. Society of American Foresters, 27, 92–99.Google Scholar
  6. Johnson, S., Whiteaker, T., & Maidment, D. (2009). A tool for automated load duration curve creation. Journal of the American Water Resources Association, 45(3), 654–663.CrossRefGoogle Scholar
  7. Kim, J., Engel, B. A., Park, Y. S., Theller, L., Chaubey, I., Kong, D. S., & Lim, K. J. (2012). Development of web-based load duration curve system for analysis of total maximum daily load and water quality characteristics in a waterbody. Journal of Environmental Management, 97(4), 46–55.CrossRefGoogle Scholar
  8. Leopold, L. B. (1994). A view of the river. Cambridge: Harvard University Press.Google Scholar
  9. Malone, R. F., Saidi, H., & Wegener, K. (1984). Predictive accuracy determination applied to a linear model phosphorus loading resulting from urban runoff. Applied Mathematical Modelling, 8, 81–88.CrossRefGoogle Scholar
  10. Richards, R. P. (2004). Improving total maximum daily load with lesson learned from log-term detailed monitoring. Journal of Environmental Engineering, 130, 657–663.CrossRefGoogle Scholar
  11. Robertson, D. M., & Roerisch, E. D. (1999). Influence of various water quality sampling strategies on load estimates for small streams. Water Resources Research, 35, 3747–3759.CrossRefGoogle Scholar
  12. Shen, J., & Zhao, Y. (2010). Combined Bayesian statistics and load duration curve method for bacteria nonpoint source loading estimation. Water Research, 44, 77–84.CrossRefGoogle Scholar
  13. Stiles, T.C. (2001). A simple method to define bacteria TMDLs in Kansas. ASIWPCA/ACWF/WEF TMDL science issues conference: On-site program. St. Louis, MO, USA. pp. 375–378.Google Scholar
  14. Sullivan, J.A. (2002). Use of load duration curves for the development of nonpoint source bacteria TMDLs in Texas. ASAE proceedings of the watershed management to meet emerging TMDL regulations conference. Fort Worth, TX, USA, 355–360.Google Scholar
  15. Teague, A., Bedient, P. B., & Guven, B. (2011). Targeted application of seasonal load duration curves using multivariate analysis in two watersheds flowing into Lake Houston. Journal of the American Water Resources Association, 47(3), 620–634.CrossRefGoogle Scholar
  16. Thompson, B.A., Fitzhugh, G.R. (1985). Synthesis and analysis of Lake Pontchartrain environments, influencing factors and trends. CFI, CWR, LSU, BR, LA 70803-7503. Prepared for Louisiana Department of Environmental Quality, Office of Water Resources.Google Scholar
  17. US EPA. (2007). An approach for using load duration curves in the development of TMDLs. EPA 841-B-07-006. Washington, DC: Office of Wetlands, Oceans, and Watersheds.Google Scholar
  18. USDA ARS. (1994). State Soil Geographic (STATSGO) Data Base: Data use information. United States Department of Agriculture—Agricultural Research Service.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Abhijit Patil
    • 1
  • Zhiqiang Deng
    • 1
  • Ronald F. Malone
    • 1
  1. 1.Department of Civil and Environmental EngineeringLouisiana State UniversityBaton RougeUSA

Personalised recommendations