Prediction of Spatial Variability of Phosphorous Over the St-Esprit Watershed
- 133 Downloads
Spatial data analysis tools for predicting the variability of non-point source pollutants minimize the time, effort and cost involved in extensive and exhaustive real field data measurements. In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostatistics were used to develop the spatial variability map of soil phosphorous saturation (Psat) percentage over the St-Espirit watershed (2610 ha), located in Quebec, Canada. The Psat measured values for the 281 geo referenced land parcel units (LPU) within the watershed were interpreted and analyzed using the ArcGIS® tool. The geostatistical extension module of ArcGIS® was used for exploratory data analysis, semivariogram model fitting, and development of a Psat prediction map using the ordinary kriging technique. Using these geostatistical procedures and adjustment of lag sizes and lag intervals representing the data sets, it was estimated that the spherical semivariogram model fitted well to represent the Psat variability with residual sum square (RSS) of 0.0003 and coefficient of determination (R2) of 0.98. Further, the developed model was used to predict the Psat variability over the St. Esprit watershed using the 1605 geo-referenced LPU locations. The generated spatial variability map was geo-spatially processed with the natural drainage network and land use feature classes of the watershed to ascertain the phosphorous loading and locate vulnerable LPUs for phosphorous management. It was observed that the Psat levels were higher at the up stream locations and near the drainage channels than the locations close to watershed outlet. Also, the land pockets with more than 60% agricultural land use resulted in supra-optimal Psat values (10% > Psat < 20%), out of which 8.5 to 16.3 ha agricultural land of the St. Esprit watershed exhibited critical agro-environmental threshold Psat values (Psat > 20%). It was also revealed that, around 23.5% of the watersheds cropped area has reached these threshold levels which necessitate judicious P input management.
KeywordsArcGIS® GS+ geostatistics kriging soil phosphorous semivariogram spatial variability map
Unable to display preview. Download preview PDF.
- Borgelt, S. D., Wieda R. E. and Sudduth K. A.: 1997, ‘Geostatistical analysis of soil chemical properties from nested grids’, Applied Engineering in Agriculture 13(4), 477–483.Google Scholar
- Carpenter, S. R., Caraco N. F., Correll D. L., Howarth R. W., Sharpley A. N. and Smith V. H.: 1998, ‘Non-point pollution of surface waters with phosphorous and nitrogen’, Ecological Applications 8(3), 559–568.Google Scholar
- Deutsch, C. V. and Journel A. G.: 1992, GSLIB Geostatistical Software Library and User's Guide, Oxford Univ. Press, New York.Google Scholar
- Enright, P., Papineau, F. and Madramootoo, C. A.: 1997, Water quality and pollutant concentrations on paired agricultural watersheds in Quebec. Proceedings of the 1997 Annual Conference of the Canadian society of Agricultural Engineering, 142–151.Google Scholar
- Enright, P., Papineau F., Madramootoo, C. A. and Leger, E.: 1995, The impacts of agricultural production on water quality in two small watersheds. CSAE Paper #95-101. CSAE Annual Meeting, Ottawa, Canada.Google Scholar
- Gamma Design Software: 2005, GS+ version 5.03 beta. Gamma Design Software, Plainwell, MI.; http://www.gammadesign.com.
- Gangbazo, G., Cluis, D. and Buon, E.: 2002, ‘Suspended sediments and phosphorus transport in an agricultural watershed’, Vecteur Environnement 35, 44–53.Google Scholar
- Giroux, M. and Tran, T. S.: 1996, ‘Critères agronomiques et environnementaux liés à la disponibilité, la solubilité et la saturation en phosphore des sols agricoles du Québec’, Agrosol IX, 51–57.Google Scholar
- Golden Software: 2002, Surfer, version 8. User's Guide. Golden Software Inc., Golden, CO; http://www.goldensoftware.com.
- Hamilton, P. A. and Miller, T. L.: 2002, ‘Lessons from the National Water-Quality Assessment’, J. Soil and Water Consv. 57, 164-A–24A.Google Scholar
- Isaaks, E. H. and Srivastava, R. M.: 1989, An introduction to Applied Geostatistics, Oxford Univ. Press, New York.Google Scholar
- Johnston, K, Hoef, J. M.V., Krivoruchko, K. and Lucas, N.: 1996, Using ArcGIS Geostatistical Analysis, GIS user Manual by ESRI, NY, 120–187.Google Scholar
- Kleinman, P. J. A., Bryant, R. B., Reid, B. S., Sharpley, A. N. and Pimentel, D.: 2000. ‘Using phosphorus behavior to identify environmental thresholds’, Soil Science 165, 943–950.Google Scholar
- Lapp, P., Madramootoo, C. A., Enright, P., Papineau, F. and Perrone, J.: 1998, ‘Water quality of an intensive agricultural watershed in Quebec’, J. of Am. Water Resources Association 34(2), 427–437.Google Scholar
- McDowell, R. W., Sharpley A. N. and Kleinman, P. J. A.: 2002, ‘Integrating phosphorus and nitrogen decision management at watershed scales, J. Am. Water Resources Association 38(2), 479– 491.Google Scholar
- Michaud, A. R. and Laverdière, M. R.: 2004, ‘Effects of cropping, soil type and manure application on phosphorus export and bio-availability’, Can. J. Soil Sci. 84(3), 295–305.Google Scholar
- Oliver, M. A.: 1999, ‘Exploring soil spatial variability geostatistically’, in: J. V. Stafford (ed.), Precision Agriculture ‘99. Proc. Eur. Conf. On Prcision Agric., 2nd, Odense, Denmark. 11–15 July 1999. Part 1. Sheffield Academic Press, Sheffield, UK, pp. 3–17.Google Scholar
- Papineau, F. and Enright, P.: 1997, Gestion de l'Eau dans le Bassin Versant de la Partie Superieure du Ruisseau St-Esprit. Project 61-13008. Charactérisation de la problématique environnementale. McGill University, Montreal, Canada.Google Scholar
- Romero, D., Madramootoo, C. A. and Enright, P.: 2002, ‘Modelling the hydrology of an agricultural watershed in Quebec using SLURP’, Canadian Biosystems Engineering 44, 1.11–1.20.Google Scholar
- Sarangi, A., Madramootoo, C. A., Singh, D. K. and Singh, A. K.: 2004, ‘Performance of GIS interfaces in watershed delineation and stream network generation from DEM’, Journal of Indian Society of Agricultural Engineering 41(2), 41–48.Google Scholar
- Sarangi, A., Cox, C. A. and Madramootoo, C. A.: 2005, ‘Geostatistical methods for prediction of spatial variability of rainfall in a mountainous region’, Transactions of ASAE 48(3), 943–954.Google Scholar
- Strahler, A. N.: 1964. Quantitative geomorphology of drainage basins and channel networks; Section 4-2, in Handbook of Applied Hydrology, ed. Ven te Chow, McGraw-Hill, New York.Google Scholar
- Webster, R.: 1985, ‘Quantitative spatial analysis of soil in the field’, Advances in Soil Sci. 3, 1–70.Google Scholar
- Webster, R. and Oliver, M. A.: 2001, Geostatistics for Environmental Scientists, John Willey and Sons Ltd, U.K., 271 pp.Google Scholar