Limits...
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

HRUs across all configurations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3824274&req=5

Fig7: HRUs across all configurations

Mentions: The HRU is a unique combination of land use pattern, soil types, and landscape attributes. The different HRUs for each soil configuration are shown in Fig. 7. There is a significant difference among the various configurations. This makes sense since the soils with higher region numbers are expected to have more soil classes and therefore higher HRUs. Since HRU is where the simulation of runoff, sediment, and nutrients starts, we should expect a proportionate increase in the magnitude of the simulations.Fig. 7


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)

HRUs across all configurations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: HRUs across all configurations
Mentions: The HRU is a unique combination of land use pattern, soil types, and landscape attributes. The different HRUs for each soil configuration are shown in Fig. 7. There is a significant difference among the various configurations. This makes sense since the soils with higher region numbers are expected to have more soil classes and therefore higher HRUs. Since HRU is where the simulation of runoff, sediment, and nutrients starts, we should expect a proportionate increase in the magnitude of the simulations.Fig. 7

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