naa a22 4500 510751105 CHVBK 20180411083104.0 cr unu---uuuuu 180411e20131001xx s 000 0 eng 10.1007/s11270-013-1692-0 doi (NATIONALLICENCE)springer-10.1007/s11270-013-1692-0 Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff, Sediment, and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties [Elektronische Daten] [Alaba Boluwade, Chandra Madramootoo] Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed "Reference”. Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling. The Author(s), 2013 Heterogeneities nationallicence Hydrology nationallicence Model averaging nationallicence Spatial variability nationallicence Water quality nationallicence Boluwade Alaba Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, H9X 3V9, Sainte-Anne-de-Bellevue, Quebec, Canada aut Madramootoo Chandra Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, H9X 3V9, Sainte-Anne-de-Bellevue, Quebec, Canada aut Water, Air, & Soil Pollution Springer Netherlands 224/10(2013-10-01), 1-16 0049-6979 224:10<1 2013 224 11270 https://doi.org/10.1007/s11270-013-1692-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11270-013-1692-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Boluwade Alaba Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, H9X 3V9, Sainte-Anne-de-Bellevue, Quebec, Canada aut NATIONALLICENCE 700 1- Madramootoo Chandra Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, H9X 3V9, Sainte-Anne-de-Bellevue, Quebec, Canada aut NATIONALLICENCE 773 0- Water, Air, & Soil Pollution Springer Netherlands 224/10(2013-10-01), 1-16 0049-6979 224:10<1 2013 224 11270 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer