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Soil and landscape attributes interpret subsurface drainage clusters

By A. Bakhsha and R. S. Kanwarb


Abstract. Water in excess of evapotranspiration follows topographically defined flow paths and can affect spatial subsurface drainage patterns. This 1993–2003 field study was conducted near Nashua, Iowa, to delineate the subsurface drainage clusters and identify the landscape and hydrologic variables that contributed significantly in discriminating these clusters. A digital elevation model was developed using 6695 elevation data measurements collected with GPS navigation system across 36 field plots (0.4 ha in size each). A spherical model was used to interpolate the elevation data within a Spatial Analyst tool of ArcGIS software. Plot-scale average topographic attributes of elevation, slope, aspect, and curvature were derived using the Zonal function in the Spatial Analyst tool. Hydrologic attributes of flow direction, flow length, and flow accumulation were derived using the Hydrology module of Spatial Analyst tool after performing Fill function for the sink areas. Annual normalised subsurface drainage data and plot-scale derived soil and topographic attributes were used in the cluster and discriminant analysis, respectively, to investigate their relationships. Stepwise discriminant analysis identified elevation and flow accumulation as the variables that discriminated the subsurface drainage clusters of low, medium, and high categories significantly (P = 0.01). The role of elevation and flow accumulation was verified using discriminant functions that predicted all members of the high drainage cluster accurately with zero error rates. GIS data layer of subsurface drainage clusters also showed that high drainage clusters were located at the lower elevation levels and were in close agreement with the elevation and flow accumulation data layers. The results of this study indicate that elevation and flow accumulation GIS data layers can be used as a guideline to minimise nutrient losses through subsurface drainage water

Topics: cluster and discriminant analysis, hydrologic modeling, DEM, GIS
Year: 2016
OAI identifier: oai:CiteSeerX.psu:
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