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Mehmet Ozturk

Publications and source records attributed to Mehmet Ozturk.

2 recordsLinked to original sources

Season, wind speed, and seasonal rain are major drivers of a regional aeolian sediment transport model

Wind erosion and sediment transport continue to increase in many parts of the world, leading to decreased soil quality, accelerated snow-melt, respiratory diseases, and traffic accidents. The processes that control sediment transport are well understood at small scales of mm to m but are less well understood at larger scales of km to hundreds of km. Here we test four approaches aimed at improving the variance explained in sediment transport measured in a network of 52 horizontal sediment flux collecting devices located on the Colorado Plateau, USA. First, switching from a regression tree to random forest statistical analysis increased the variance in sediment transport explained from 58% to 91%. Soil moisture as a single variable explained 52% of variation in sediment flux, but had a negligible effect on a random forest model with season (Winter, Spring, Summer), wind speed, and seasonal total precipitation. Similarly, adding four years of new data to an existing five-year dataset or adding measurements of soil roughness and grazing failed to improve variance explained. By explaining 91% of the variance in sediment transport, our model provides baseline model for understanding sediment transport on the landscape scale. Dust flux networks in new regions would likely need to collect at least 300-500 samples to describe variation in sediment transport values using random forest analyses of the effects of season, wind speed, seasonal rain and vegetation type.

Colorado, Utah

Acoustic doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay

A data set was acquired on a shallow mudflat in south San Francisco Bay that featured simultaneous, co-located optical and acoustic sensors for subsequent estimation of suspended sediment concentrations (SSC). The optical turbidity sensor output was converted to SSC via an empirical relation derived at a nearby site using bottle sample estimates of SSC. The acoustic data was obtained using an acoustic Doppler velocimeter. Backscatter and noise were combined to develop another empirical relation between the optical estimates of SSC and the relative backscatter from the acoustic velocimeter. The optical and acoustic approaches both reproduced similar general trends in the data and have merit. Some seasonal variation in the dataset was evident, with the two methods differing by greater or lesser amounts depending on which portion of the record was examined. It is hypothesized that this is the result of flocculation, affecting the two signals by different degrees, and that the significance or mechanism of the flocculation has some seasonal variability. In the earlier portion of the record (March), there is a clear difference that appears in the acoustic approach between ebb and flood periods, and this is not evident later in the record (May). The acoustic method has promise but it appears that characteristics of flocs that form and break apart may need to be accounted for to improve the power of the method. This may also be true of the optical method: both methods involve assuming that the sediment characteristics (size, size distribution, and shape) are constant

California