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In this study, the conformity of mathematical model of the MUSLE for the estimation of storm-wise sediment yield in plots was examined at Matash Ranch in northern Iran. For the measurement of sediment yield, three plots were established within each openly grazed and manually harvested area. The MUSLE model was applied through determining the entire required inputs for the study plots of 24 storm events, which occurred during the study period from May to September 2004. The quantity results were then statistically compared using paired t-Test analysis. The results of the study verified the disability of the model in sound prediction of sediment yield on storm basis for the study area. Additionally, a high level of agreement beyond 86% was found between estimated and measured sediment yield which created the possibility of comparative evaluation of the effects of different land use managerial approaches on sediment yield in the study area.
In this study, the conformity of mathematical model of the MUSLE for the estimation of storm-wise sediment yield in plots was examined at Matash Ranch in northern Iran. For the measurement of sediment yield, three plots were established within each openly grazed and manually harvested area. The MUSLE model was applied through determining the entire required inputs for the study plots of 24 storm events, which occurred during the study period from May to September 2004. The quantity results were then statistically compared using paired t-Test analysis. The results of the study verified the disability of the model in sound prediction of sediment yield on storm basis for the study area. Additionally, a high level of agreement beyond 86% was found between estimated and measured sediment yield which created the possibility of comparative evaluation of the effects of different land use managerial approaches on sediment yield in the study area.
Daily total suspended solids concentrations (TSS, mg L -1 ), yields (Y, kg day -1 km -2 ) and runoff (q, L s -1 km -2 ) in world rivers are described by the median (C50), the upper percentile (C99), the dischargeweighted average concentrations (C*), and by their corresponding yields (Y50, Y99, Y*) and runoff (q*, q50, q99). These intra-station descriptors range over two to six orders of magnitude at a given station. Inter-station variability is considered through three sets of dimensionless metrics: (i) q*/q50, C*/C50 and Y*/Y50, defining the general temporal variability indicators, and q99/q50, C99/C50 and Y99/Y50, defining the extreme variability indicators; (ii) river flow duration (W2) and flux duration (M2) in 2% of time; and (iii) the truncated rating curve exponent (b50sup) of the C vs q relationship for the upper flows. The TSS and Y variability, measured on US, French and world rivers, are first explained by hydrological variability through the b50sup metric, the variability amplifier, then by basin size, erodibility, relief and lake occurrence. Yield variability is the product of runoff variability × TSS variability. All metrics are considerably modified after river damming. The control of river particulate matter (RPM) composition by TSS or yields depends on the targeted component. For major elements (Al, Fe, Mn, Ti, Si, Ca, Mg, Na, K), the average RPM chemistry is not dependent on C* and Y* in most world hydroregions, except in the tropical hydrobelt where it is controlled by basin relief. By contrast, the particulate organic carbon content (POC, as a percentage of RPM) is inversely correlated to TSS concentrations for (i) intra-station measurements in any hydroregion, and (ii) inter-station average POC and TSS figures in world rivers. TSS controls heavy metal content (ppm) in highly contaminated basins (e.g. Cd in the Seine vs the Rhone), and total metal concentration (ng/L) in all cases. Relations between RPM composition and TSS should be taken into account when assessing riverine fluxes, as ignoring them could lead to overestimation.
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