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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.

Boluwade A, Madramootoo C - Water Air Soil Pollut (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, Quebec H9X 3V9 Canada.

ABSTRACT
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-km(2) 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.

No MeSH data available.


Related in: MedlinePlus

Heterogeneous soil clusters derived using REDCAP a 15 and b 20 regions
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Fig4: Heterogeneous soil clusters derived using REDCAP a 15 and b 20 regions

Mentions: The result of the spatial division of the study area into 24 polygons using the Thiessen polygon technique in ArcGIS 10 is illustrated in Fig. 2. Each polygon has unique soil properties. We clustered these proximal zones into 24, 20, 15, 10, and 5 heterogeneous zones. The clusters and derived soil maps can be seen in Figs. 3, 4, and 5. Within-region heterogeneity of each of the regions is shown in Fig. 6. The SSD measure of within-region heterogeneity indicated that the configurations with the least number of regions (Region_5) were the most heterogeneous while the lowest SSD value was found for Region_24. In other words, the smallest SSD value was obtained when each unit (sample point) was a region in itself. This makes the region mean the same as the unit mean. Therefore, the greater number of regions, the less heterogeneous the region is. These maps were used as soil inputs into SWAT to quantify the impact of heterogeneities of the measured soil properties.Fig. 2


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.

Boluwade A, Madramootoo C - Water Air Soil Pollut (2013)

Heterogeneous soil clusters derived using REDCAP a 15 and b 20 regions
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3824274&req=5

Fig4: Heterogeneous soil clusters derived using REDCAP a 15 and b 20 regions
Mentions: The result of the spatial division of the study area into 24 polygons using the Thiessen polygon technique in ArcGIS 10 is illustrated in Fig. 2. Each polygon has unique soil properties. We clustered these proximal zones into 24, 20, 15, 10, and 5 heterogeneous zones. The clusters and derived soil maps can be seen in Figs. 3, 4, and 5. Within-region heterogeneity of each of the regions is shown in Fig. 6. The SSD measure of within-region heterogeneity indicated that the configurations with the least number of regions (Region_5) were the most heterogeneous while the lowest SSD value was found for Region_24. In other words, the smallest SSD value was obtained when each unit (sample point) was a region in itself. This makes the region mean the same as the unit mean. Therefore, the greater number of regions, the less heterogeneous the region is. These maps were used as soil inputs into SWAT to quantify the impact of heterogeneities of the measured soil properties.Fig. 2

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioresource Engineering, Macdonald Stewart Building, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, Quebec H9X 3V9 Canada.

ABSTRACT
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-km(2) 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.

No MeSH data available.


Related in: MedlinePlus