2012Hydrology researchOpen access

Evaluation of spatial variability on hydrology and nutrient source loads at watershed scale using a modeling approach

Prem B. Parajuli

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Abstract

Understanding the effects of spatial variability on hydrologic parameters and nutrient source load distribution is essential to develop water quality improvement programs. The objective of this research was to evaluate spatially distributed hydrologic variability, nutrient sources, and their loadings at the watershed scale using a modeling approach. The Soil and Water Assessment Tool (SWAT) was applied to assess spatial variability of annual average water, sediment, total phosphorus (TP), and total nitrogen (TN) yields from the Upper Pearl River Watershed (UPRW) in Mississippi, USA. The SWAT model was successfully calibrated, validated, and verified with good model efficiency (R2 = 0.70 and E = 0.59) using monthly measured stream flow, daily observed flow, sediment, TP, and TN yields. The SWAT model results determined that spatial variability of annual average pollutant loads of water yield ranged from 877 to 206 mm, sediment yield ranged from 1.71 to 0.17 t ha−1, TP ranged from 1.39 to 0.02 kg ha−1, and TN ranged from 10.22 to 0.69 kg ha−1 in the watershed sub-basins. Understanding of the spatial variability of hydrologic and nutrient source loads distribution helps watershed managers focus their management efforts to the most needy watershed sub-basins.

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Understanding the effects of spatial variability on hydrologic parameters and nutrient source load distribution is essential to develop water quality improvement programs. The objective of this research was to evaluate spatially distributed hydrologic variability, nutrient sources, and their loadings at the watershed scale using a modeling approach. The Soil and Water Assessment Tool (SWAT) was applied to assess spatial variability of annual average water, sediment, total phosphorus (TP), and total nitrogen (TN) yields from the Upper Pearl River Watershed (UPRW) in Mississippi, USA. The SWAT model was successfully calibrated, validated, and verified with good model efficiency (R2 = 0.70 and E = 0.59) using monthly measured stream flow, daily observed flow, sediment, TP, and TN yields. The SWAT model results determined that spatial variability of annual average pollutant loads of water yield ranged from 877 to 206 mm, sediment yield ranged from 1.71 to 0.17 t ha−1, TP ranged from 1.39 to 0.02 kg ha−1, and TN ranged from 10.22 to 0.69 kg ha−1 in the watershed sub-basins. Understanding of the spatial variability of hydrologic and nutrient source loads distribution helps watershed managers focus their management efforts to the most needy watershed sub-basins.

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Available abstract

Understanding the effects of spatial variability on hydrologic parameters and nutrient source load distribution is essential to develop water quality improvement programs. The objective of this research was to evaluate spatially distributed hydrologic variability, nutrient sources, and their loadings at the watershed scale using a modeling approach. The Soil and Water Assessment Tool (SWAT) was applied to assess spatial variability of annual average water, sediment, total phosphorus (TP), and total nitrogen (TN) yields from the Upper Pearl River Watershed (UPRW) in Mississippi, USA. The SWAT model was successfully calibrated, validated, and verified with good model efficiency (R2 = 0.70 and E = 0.59) using monthly measured stream flow, daily observed flow, sediment, TP, and TN yields. The SWAT model results determined that spatial variability of annual average pollutant loads of water yield ranged from 877 to 206 mm, sediment yield ranged from 1.71 to 0.17 t ha−1, TP ranged from 1.39 to 0.02 kg ha−1, and TN ranged from 10.22 to 0.69 kg ha−1 in the watershed sub-basins. Understanding of the spatial variability of hydrologic and nutrient source loads distribution helps watershed managers focus their management efforts to the most needy watershed sub-basins.

Key concepts: Soil and Water Assessment Tool, Watershed, Environmental science, Hydrology (agriculture), SWAT model, Spatial variability, Surface runoff, Water quality

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