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Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River

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Abstract

Daily nitrate-nitrogen (NO3-N) loads in the Raccoon River, Iowa, were estimated using Ordinary kriging (OK), Cokriging (CK), and a standard rating curve method (LOADEST) based on a dataset of 3451 measurements of NO3-N concentration collected over 19 years. The CK estimation utilizes the temporal correlation of NO3-N load with daily discharge and honors the measured points to improve estimation relative to regression based models. Loads were estimated using the observed concentrations and three subsets of the measured data that correspond to three frequencies (weekly, biweekly, and monthly). Results indicated that daily NO3-N loads were best estimated by CK using measured loads with daily discharge. Daily load estimates produced by OK using weekly data matched well with measured values, but discrepancies emerged when samples were collected less frequently, e.g., biweekly and monthly. For the entire 19-year dataset, compared to measured loads, the estimated total NO3-N load decreased using OK when samples were collected monthly, but increased using CK. Load estimation using the seven-parameter LOADEST model did not perform well for the Raccoon River because the correlation of NO3-N concentration to river discharge was poor. For the site studied, weekly and biweekly sampling may be sufficient to estimate daily NO3-N loads with CK when daily discharge data is available.

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References

  • Alexander RB, Smith RA, Schwarz GE, Boyer EW, Nolan JV, Brakebill JW (2008) Differences in phosphorus and nitrogen delivery to the gulf of Mexico from the Mississippi river basin. Environ Sci Technol 42:822–830. doi:10.1021/Es0716103

    Article  Google Scholar 

  • Basaran M, Erpul G, Ozcan AU, Saygin DS, Kibar M, Bayramin I, Yilman FE (2011) Spatial information of soil hydraulic conductivity and performance of Cokriging over Kriging in a semi-arid basin scale. Environ Earth Sci 63:827–838. doi:10.1007/s12665-010-0753-6

    Article  Google Scholar 

  • Cochran WG (1977) Sampling techniques. 3rd In Symposium on census and inventory

  • Cressie NAC (1991) Statistics for spatial data. Wiley, New York

    Google Scholar 

  • Diaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems. Science 321:926–929. doi:10.1126/science.1156401

    Article  Google Scholar 

  • Diodato N, Tartari G, Bellocchi G (2010) Geospatial rainfall modelling at eastern Nepalese highland from ground environmental data. Water Resour Manag 24(11):2703–2720. doi:10.1007/s11269-009-9575-2

    Article  Google Scholar 

  • Dodds WK, Welch EB (2000) Establishing nutrient criteria in streams. J N Am Benthol Soc 19:186–196. doi:10.2307/1468291

    Article  Google Scholar 

  • Greene WH (1997) Econometric analysis 3rd edition. Macmillan Publishing Company

  • Helsel DR, Hirsch RM (Eds.) (2002) Statistical methods in water resources. In: Techniques of Water Resources Research, book 4, chap. A3, 510 pp., U.S. Geol. Surv., Washington, D. C.

  • Iowa Nutrient Reduction Strategy (INRS) (2013) http://www.nutrientstrategy.iastate.edu/. Retrieved 6 Jan 2016

  • Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford University Press, New York

    Google Scholar 

  • Johnson AH (1979) Estimating solute transport in streams from grab samples. Water Resour Res 15:1224–1228. doi:10.1029/Wr015i005p01224

    Article  Google Scholar 

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic, London

    Google Scholar 

  • Kitanidis PK, Vomvoris EG (1983) A geostatistical approach to the inverse problem in groundwater modeling (Steady-State) and one-dimensional simulations. Water Resour Res 19:677–690. doi:10.1029/Wr019i003p00677

    Article  Google Scholar 

  • Kuhlman KL, Iguzquiza EP (2010) Universal cokriging of hydraulic heads accounting for boundary conditions. J Hydrol 384:14–25. doi:10.1016/j.jhydrol.2010.01.002

    Article  Google Scholar 

  • Larson SJ, Capel PD, Goolsby DA (1995) Relations between pesticide use and riverine flux in the Mississippi river basin. Chemosphere 31:3305–3321. doi:10.1016/0045-6535(95)00176-9

    Article  Google Scholar 

  • Li BL, Yeh TCJ (1999) Cokriging estimation of the conductivity field under variably saturated flow conditions. Water Resour Res 35:3663–3674. doi:10.1029/1999wr900268

    Article  Google Scholar 

  • Li ZW, Zhang YK, Schilling K, Skopec M (2006) Cokriging estimation of daily suspended sediment loads. J Hydrol 327:389–398. doi:10.1016/j.jhydrol.2005.11.028

    Article  Google Scholar 

  • Mukhopadhyay B, Smith EH (2000) Comparison of statistical methods for estimation of nutrient load to surface reservoirs for sparse data set: application with a modified model for phosphorus availability. Water Res 34:3258–3268. doi:10.1016/S0043-1354(00)00062-2

    Article  Google Scholar 

  • Preston SD, Bierman VJ, Silliman SE (1989) An evaluation of methods for the estimation of tributary mass loads. Water Resour Res 25:1379–1389. doi:10.1029/Wr025i006p01379

    Article  Google Scholar 

  • Quilbe R, Rousseau AN, Duchemin M, Poulin A, Gangbazo G, Villeneuve JP (2006) Selecting a calculation method to estimate sediment and nutrient loads in streams: application to the Beaurivage River (Quebec, Canada). J Hydrol 326:295–310. doi:10.1016/j.jhydrol.2005.11.008

    Article  Google Scholar 

  • Robertson GP (2008) GS+: geostatistics for the environmental sciences. Gamma Design Software, Plainwell

    Google Scholar 

  • Runkel RL, Crawford CG, Cohn TA (2004) Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers: U.S. Geological Survey Techniques and Methods Book 4, Chapter A5, 69 p

  • Schilling KE, Lutz DS (2004) Relation of nitrate concentrations to baseflow in the Raccoon River, Iowa. J Am Water Resour Assoc 40:889–900. doi:10.1111/j.1752-1688.2004.tb01053.x

    Article  Google Scholar 

  • Shiau JT, Chen TJ (2015) Quantile regression-based probabilistic estimation scheme for daily and annual suspended sediment loads. Water Resour Manag 29(8):2805–2818. doi:10.1007/s11269-015-0971-5

    Article  Google Scholar 

  • Sivakumar B, Wallender WW (2004) Deriving high-resolution sediment load data using a nonlinear deterministic approach. Water Resour Res 40 W05403. doi: 10.1029/2004wr003152

  • Stenback GA, Crumpton WG, Schilling KE, Helmers MJ (2011) Rating curve estimation of nutrient loads in Iowa rivers. J Hydrol 396:158–169. doi:10.1016/j.jhydrol.2010.11.006

    Article  Google Scholar 

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Acknowledgments

This work was made possible by resources contributed by the Iowa Department of Natural Resources, the National Nature Science Foundation of China (NSFC-41272260, NSFC-41330314, NSFC-41302180, and NSFC-41203062), the national project “Water Pollution Control” of China (2012ZX07204-001, 2012ZX07204-003), the Jiangsu province scientific and technological project (BE2015708, SBK201341336), and the Iowa Soybean Association. The authors are grateful to the Editor, Associate Editor, and the two reviewers for their encouraging, insightful and constructive comments.

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Correspondence to You-Kuan Zhang.

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Liang, X., Schilling, K., Zhang, YK. et al. Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River. Water Resour Manage 30, 1771–1784 (2016). https://doi.org/10.1007/s11269-016-1250-9

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