Skip to main content

Advertisement

SpringerLink
Log in

Influence of different nitrate–N monitoring strategies on load estimation as a base for model calibration and evaluation

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Model-based predictions of the impact of land management practices on nutrient loading require measured nutrient flux data for model calibration and evaluation. Consequently, uncertainties in the monitoring data resulting from sample collection and load estimation methods influence the calibration, and thus, the parameter settings that affect the modeling results. To investigate this influence, we compared three different time-based sampling strategies and four different load estimation methods for model calibration and compared the results. For our study, we used the river basin model Soil and Water Assessment Tool on the intensively managed loess-dominated Parthe watershed (315 km2) in Central Germany. The results show that nitrate–N load estimations differ considerably depending on sampling strategy, load estimation method, and period of interest. Within our study period, the annual nitrate–N load estimation values for the daily composite data set have the lowest ranges (between 9.8% and 15.7% maximum deviations related to the mean value of all applied methods). By contrast, annual estimation results for the submonthly and the monthly data set vary in greater ranges (between 24.9% and 67.7%). To show differences between the sampling strategies, we calculated the percentage deviation of mean load estimations of submonthly and monthly data sets as related to the mean estimation value of the composite data set. For nitrate–N, the maximum deviation is 64.5% for the submonthly data set in the year 2000. We used average monthly nitrate–N loads of the daily composite data set to calibrate the model to achieve satisfactory simulation results [Nash–Sutcliffe efficiency (NSE) 0.52]. Using the same parameter settings with submonthly and monthly data set, the NSE dropped to 0.42 and 0.31, respectively. Considering the different results from the monitoring strategy and the load estimation method, we recommend both the implementation of optimized monitoring programs and the use of multiple load estimation methods to improve water quality characterization and provide appropriate model calibration and evaluation data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allan, I. J., Brana, B., Greenwood, R., Mills, G. A., Roig, B., & Gonzalez, C. (2006a). A “toolbox” for biological and chemical monitoring requirements for the European union’s water framework directive. Talanta, 69, 302–322.

    Article  CAS  Google Scholar 

  • Allan, I. J., Mills, G. A., Vrana, B., Knutsson, J., Holmberg, A., Guigues, N., et al. (2006b). Strategic monitoring for the European Framework Directive. Trends in Analytic Chemistry, 25(7), 704–715.

    Article  CAS  Google Scholar 

  • Arnold, J. G., & Allen, P. (1996). Estimating hydrologic budgets for three Illinois watersheds. Journal of Hydrology, 176, 57–77.

    Article  Google Scholar 

  • Arnold, J. G., & Fohrer, N. (2005). SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrological Processes, 19, 563–572.

    Article  Google Scholar 

  • Arnold, J. G., Allen, P. M., Muttiah, R., & Bernhardt, G. (1995). Automated base flow separation and recession analysis techniques. Ground Water, 33(6), 1010–1017

    Article  CAS  Google Scholar 

  • Behera, S., & Panda, R. K. (2006). Evaluation of management alternatives for an agricultural watershed in a sub-humid subtropical region using a physical process based model. Agriculture, Ecosystems & Environment, 113, 62–72.

    Article  Google Scholar 

  • Behrendt, H., Huber, P., Opitz, D., Scholz, G., & Uebe, R. (1999). Nährstoffbilanzierung der Flussgebiete Deutschlands. UBA-Bericht. Inst. f. Gewässerökologie und Binnenfischerei im Forschungsverbund Berlin e. V., Berlin.

  • Bracmort, K. S., Arabi, M., Frankenberger, J. R., Engel, B. A., & Arnold, J. G. (2006). Modeling long-term water quality impact of structural BMPs. Transactions ASABE, 49(2), 367–374.

    CAS  Google Scholar 

  • Chaplot, V., Saleh, A., Jaynes, D. B., & Arnold, J. (2004). Predicting water, sediment and NO3-N loads under scenarios of land-use and management practices in a flat watershed. Water, Air and Soil Pollution, 154, 271–293.

    Article  CAS  Google Scholar 

  • Di Luzio, M., Srinivasan, R., & Arnold, J. G. (2004). A GIS-coupled hydrological model system for the watershed assessment of agricultural nonpoint and point sources of pollution. Transactions in Geographic Information Systems, 8(1), 113–136.

    Google Scholar 

  • EEB & WWF (2005). European Environmental Bureau and WWF. European Policy Office: EU Water Policy: Making the Water Framework Directive. The quality of national transposition and implementation of the Water Framework Directive at the end of 2004. Brussels. http://www.eeb.org. Accessed 25 March 2005.

  • Ferguson, R. I. (1987). Accuracy and precision of methods for estimating river loads. Earth Surface Processes and Landforms, 12, 95–104.

    Article  Google Scholar 

  • Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The soil and water assessment tool: Historical development, applications, and future research directions. Transactions ASABE, 50(4), 1211–150.

    CAS  Google Scholar 

  • Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135–143.

    Article  Google Scholar 

  • Haferkorn, U., Müller, K., Mellentin, U., & Fahl, J. (2003). Möglichkeiten und Grenzen der Stofftransport-modellierung (Nitrat) am Beispiel des Parthegebietes. Abschlußbericht zum Teilthema: “Bestimmung des Nitratstromes im Grund und Oberflächenwasser mit dem Modell Part auf Basis von PCGEOFIM”.

  • Harmel, R. D., Cooper, R. J., Slade, R. M., Haney, R. L., & Arnold, J. G. (2006a). Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Transactions ASABE, 49(3), 689–701.

    CAS  Google Scholar 

  • Harmel, R. D., & King, K. W. (2005). Uncertainty in measured sediment and nutrient flux in runoff from small agricultural watersheds. Transactions ASAE, 48(5), 1713–1721.

    CAS  Google Scholar 

  • Harmel, R. D., King, K. W., Haggard, B. E., Wren, D. G., & Sheridan, J. M. (2006b). Practical guidance for discharge and water quality data collection on small watersheds. Transactions ASABE, 49(4), 937–948.

    Google Scholar 

  • Harmel, R. D., King, K. W., & Slade, R. M. (2003). Automated storm water sampling on small watersheds. Applied Engineering in Agriculture, 19, 667–674.

    Google Scholar 

  • Harmel, R. D., King, K. W., Wolfe, J. E., & Torbert, H. A. (2002). Minimum flow considerations for automated storm sampling on small watersheds. Texas Journal of Science, 54(2), 177–188.

    Google Scholar 

  • Harmel, R. D., Smith, D. R., King, K. W., & Slade, R. M. (2009). Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modelling applications. Environmental Modelling & Software, 24, 832–842.

    Article  Google Scholar 

  • Izuno, F. T., Rice, R. W., Garcia, R. M., Capone, L. T., & Downey, D. (1998). Time versus flow composite water sampling for regulatory purposes in the everglades agricultural area. Applied Engineering in Agriculture, 14(3), 257–266.

    Google Scholar 

  • Jarvie, H. P., Neal, C., & Tappin, A. D. (1997). European land-based pollutant loads to the North Sea: An analysis of the Paris Commission data and review of monitoring strategies. The Science of the Total Environment, 194–195, 39–58.

    Google Scholar 

  • Jörgensen, L. F., Refsgaard, J. Ch., & Højberg, A. L. (2007). The inadequacy of monitoring without modelling support. Journal of Environmental Monitoring, 9, 931–942.

    Article  CAS  Google Scholar 

  • Keller, M., Hilden, M., & Joost, M. (1997). Vergleich von Schätzmethoden für jährliche Stofffrachten am Beispiel des IKSR-Messprogrammes 1995 (BfG-1078), BMU-Maßnahme Nr. 4: Weitergehende Bewertung von Gewässergütedaten. JAP-BfG (1997), 413.

  • King, K. W., & Harmel, R. D. (2003). Considerations in selecting a water quality sampling strategy. Transactions ASAE, 46(1), 63–73.

    Google Scholar 

  • Littlewood, I. G. (1995). Hydrological regimes, sampling strategies, and assessment of errors in mass load estimates for United Kingdom rivers. Environment International, 21(2), 211–220.

    Article  CAS  Google Scholar 

  • Littlewood, I. G., & Marsh, T. J. (2005). Annual freshwater river mass loads from Great Britain, 1975–1994: Estimation algorithm, database and monitoring network issues. Journal of Hydrology, 305, 221–237.

    Article  CAS  Google Scholar 

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions ASABE, 50(3), 885–900.

    Google Scholar 

  • Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98–99, 257–277.

    Article  Google Scholar 

  • Nash, J. E., & Suttcliffe, J. V. (1970). River flow forecasting through conceptual models, Part I. A discussion of principles. Journal of Hydrology, 10(3), 282–290.

    Article  Google Scholar 

  • Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J. R. (2002). Soil and Water Assessment Tool. User’s manual, version 2000. GSWRL Report 02-02, BRC Report 2-06, Temple, TX, USA.

  • OSPAR (2004). OSPAR Guidelines for Harmonised Quantification and Reporting Procedures for Nutrients (HARP-NUT): Guideline 7: Quantification and reporting of the monitored riverine load of nitrogen and phosphorus, including flow normalisation procedures. Reference number: 2004-2-E.

  • Pandey, V. K., Panda, S. N., & Sudhakar, S. (2005). Modelling of an agricultural watershed using remote sensing and a geographic information system. Biosystems Engineering, 90(3), 331–347.

    Google Scholar 

  • Robertson, D. M. (2003). Influence of different temporal sampling strategies on estimating total phosphorus and suspended sediment concentration and transport in small streams. Journal of the American Water Resources Association, 39(5), 1281–1308.

    Article  Google Scholar 

  • Robertson, D. M., & Roerish, E. D. (1999). Influence of various water quality sampling strategies on load estimates for small streams. Water Resources Research, 35(12), 3747–3759.

    Article  CAS  Google Scholar 

  • Schwarze, R. (1985). Gegliederte analyse und synthese des Niederschlag-Abfluss-Prozesses von Einzugsgebieten. Dissertation, Dresden University of Technology.

  • SMUL (Sächsisches Staatsministerium für Umwelt und Landwirtschaft) (2005). http://www.smul.de/de/wu/umwelt/wasser/inhalt_re_822_823.html. Accessed 18 July 2005.

  • Stone, K. C., Hunt, P. G., Novak, J. M., Johnson, M. H., & Watts, D. W. (2000). Flow-proportional, time-composited, and grab sample estimation of nitrogen export from an Eastern Coastal Plain Wastershed. Transactions ASAE, 43(2), 281–290.

    CAS  Google Scholar 

  • Swistock, B. R., Edwards, P. J., Wood, F., & Dewalle, D. R. (1997). Comparison of methods for calculating annual solute exports from six forested Appalachian watersheds. Hydrological Processes, 11, 655–669.

    Article  Google Scholar 

  • Tate, K. W., Dahlgren, R. A., Singer, M. J., & Allen-Diaz, B. (1999). Timing, frequency of sampling affect accuracy of water-quality monitoring. California Agriculture, 53(6), 44–48.

    Article  Google Scholar 

  • Vandenberghe, A., Goethals, P. L. M., van Griensven, A., Meirlaen, J., de Pauw, N., Vanrolleghem, P., et al. (2006). Application of automated measurement stations for continuous water quality monitoring of the Dender River in Flanders, Belgium. Environmental Monitoring and Assessment, 108, 85–98.

    Article  CAS  Google Scholar 

  • Verhoff, F. H., Yakisch, S. M., & Melfi, D. A. (1980). River nutrient and chemical transport estimation. Journal of the Environmental Engineering Division ASCE, 10(6), 591–608.

    Google Scholar 

  • Volk, M., Liersch, S., & Schmidt, G. (2009). Towards the implementation of the European Water Framework Directive? Lessons learned from water quality simulations in an agricultural watershed. Land Use Policy, 26(3), 580–588.

    Article  Google Scholar 

  • Walling, D. E., & Webb, B. W. (1981). The reliability of suspended sediment load data. Proceedings of the Florence Symposium, June1981. IAHS Publications, 133, 177–194.

    Google Scholar 

  • Webb, B. W., Phillips, J. M., Walling, D. E., Littlewood, I. G., Watts, C. D., & Leeks, G. J. L. (1997). Load estimation methodologies for British rivers and their relevance to the LOIS RACS(R) programme. Science of the Total Environment, 194–195, 379–389.

    Article  Google Scholar 

  • Wendland, F., & Kunkel, R. (1999). Das Nitratabbauvermögen im Grundwasser des Elbeeinzugsgebietes. Forschungszentrum Jülich.

Download references

Author information

Authors and Affiliations

  1. Department of Computational Landscape Ecology, UFZ, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany

    Antje Ullrich & Martin Volk

Authors
  1. Antje Ullrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Volk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antje Ullrich.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ullrich, A., Volk, M. Influence of different nitrate–N monitoring strategies on load estimation as a base for model calibration and evaluation. Environ Monit Assess 171, 513–527 (2010). https://doi.org/10.1007/s10661-009-1296-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-009-1296-8

Keywords

Search

Navigation