Biological assessments of streams that have been adversely impacted by sediment runoff in Idaho, USA

Biological assessments of streams that have been adversely impacted by sediment runoff in Idaho, USA Sediments are the major source of pollution in surface waters of the Pacific Northwest Region of the USA. The purpose of this study is to evaluate the relationship between SMI water quality scores at 76 sampling sites in eight watersheds and the observed soil erosion rates on adjacent landscapes. The water quality SMI scores in streams were obtained using stream macro invertebrates as an indicator of water quality, while soil erosion rates were determined by observation on adjoining landscapes during periods of maximum precipitation. Soil erosion rates of >2, 2-5, 5-15, 15-25 and <25 mt/ha/yr were observed at 9, 20, 45, 14 and 12% of the sampling sites, respectively. Landscapes with erosion rates of less than 5 mt/ha/yr generally resulted in good water quality in adjacent streams; however, when soil erosion rates on adjacent landscapes exceeded 5 mt/ha/yr SMI water quality scores were less than good 86% of the time. Strong significant relationships were observed between SMI water quality rating and observed soil erosion rates. Consequently, land management or rehabilitation practices that reduce soil erosion rates to levels below 5 mt/ha//yr should improve stream water quality.

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Overland Flow, Soil Erosion and Stream Water Quality in Forest Under Different Perturbations and Climate Conditions

Forest Management and the Water Cycle - Ecological Studies ◽

10.1007/978-90-481-9834-4_15 ◽

2010 ◽

pp. 263-289 ◽

Cited By ~ 1

Author(s):

Meni Ben-Hur ◽

Cristina Fernandez ◽

Sakari Sarkkola ◽

Juan Carlos Santamarta Cerezal

Keyword(s):

Water Quality ◽

Soil Erosion ◽

Overland Flow ◽

Stream Water ◽

Stream Water Quality ◽

Climate Conditions

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Improving the global applicability of the RUSLE model – adjustment of the topographical and rainfall erosivity factors

Geoscientific Model Development ◽

10.5194/gmd-8-2893-2015 ◽

2015 ◽

Vol 8 (9) ◽

pp. 2893-2913 ◽

Cited By ~ 33

Author(s):

V. Naipal ◽

C. Reick ◽

J. Pongratz ◽

K. Van Oost

Keyword(s):

High Resolution ◽

Soil Erosion ◽

Global Scale ◽

Rainfall Erosivity ◽

Large Spatial Scale ◽

Rusle Model ◽

Erosion Rates ◽

Coarse Resolution ◽

Soil Erosion Rates ◽

The Usa

Abstract. Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale. This limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is, due to its simple structure and empirical basis, a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial-scale applications often rely on coarse data input, which is not compatible with the local scale on which the model is parameterized. Our study aims at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting erosion rates to extensive empirical databases from the USA and Europe. By scaling the slope according to the fractal method to adjust the topographical factor, we managed to improve the topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that compared well to high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted version shows a global higher mean erosion rate and more variability in the erosion rates. Comparison to empirical data sets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional differences with the empirical databases, the results indicate that the methods used here seem to be a promising tool in improving the applicability of the RUSLE model at coarse resolution on a global scale.

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Analysis of Influence on Stream Water Quality by Soil Erosion Control Structures

Journal of Korean Forestry Society ◽

10.14578/jkfs.2013.102.4.571 ◽

2013 ◽

Vol 102 (4) ◽

pp. 571-577

Author(s):

Jae-Hyeon Park ◽

Choonsig Kim

Keyword(s):

Water Quality ◽

Soil Erosion ◽

Stream Water ◽

Erosion Control ◽

Stream Water Quality ◽

Control Structures ◽

Soil Erosion Control

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Improving the global applicability of the RUSLE model – adjustment of the topographical and rainfall erosivity factors

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-2991-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 2991-3035 ◽

Cited By ~ 6

Author(s):

V. Naipal ◽

C. Reick ◽

J. Pongratz ◽

K. Van Oost

Keyword(s):

High Resolution ◽

Soil Erosion ◽

Global Scale ◽

Rainfall Erosivity ◽

Large Spatial Scale ◽

Rusle Model ◽

Erosion Rates ◽

Coarse Resolution ◽

Soil Erosion Rates ◽

The Usa

Abstract. Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale, and this limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is due to its simple structure and empirical basis a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial scale applications often rely on coarse data input, which is not compatible with the local scale at which the model is parameterized. This study aimed at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting soil erosion rates to extensive empirical databases on soil erosion from the USA and Europe. Adjusting the topographical factor required scaling of slope according to the fractal method, which resulted in improved topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that are in good comparison with high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted RUSLE model not only shows a global higher mean soil erosion rate but also more variability in the soil erosion rates. Comparison to empirical datasets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional differences with the empirical databases, the results indicate that the methods used here seem to be a promising tool in improving the applicability of the RUSLE model on a coarse resolution on global scale.

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