Sources of stream bed sediment in an urbanized watershed

Malhotra, Kritika; Lamba, Jasmeet; Shepherd, Stephanie L.

doi:10.1016/j.catena.2019.104228

Cited by 15 publications

(11 citation statements)

References 53 publications

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“…The results of this study show that the relative source contributions from different sources to suspended sediment and stream bed sediment might not be always similar. For example, a previous study conducted by the authors in this watershed [85] showed that the dominant sources of bed sediment (for both the particle sizes, 63-212 µm and <63 µm) at site 1 and 2 were stream banks. However, for suspended sediment, construction sites were the dominant sources of suspended sediment at site 1, whereas both stream banks and construction sites were the important sources of suspended sediment at site 2.…”

Section: Comparison Between Suspended and Stream Bed Sediment Sourcesmentioning

confidence: 89%

Fingerprinting Suspended Sediment Sources in an Urbanized Watershed

Malhotra

Lamba

Srivastava

et al. 2018

Water

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The elevated supply of fine-grained sediment to a river system negatively impacts the water quality and ecosystem health. Therefore, quantification of the relative contribution from different sources to in-stream sediment is of major interest to target sediment mitigation best management practices (BMPs). The objective of this study was to determine the relative contribution from different sources of suspended sediment in an urbanized watershed (31 km2) located in the eastern part of Alabama, USA. Estimates of relative contributions from individual source types were assessed for two different particle size fractions, 63–212 μm (fine sand) and <63 μm (silt and clay). Results of this study indicate that the construction sites were the dominant source of suspended sediment in this watershed. The average annual subwatershed-level surface runoff determined using the Soil and Water Assessment Tool (SWAT) model varied from 2.3 to 11,980 mm ha−1 year−1. Areas that generate high surface runoff have the potential to contribute disproportionately high amounts of sediment to streams and therefore should be targeted for BMPs. The results of this study show that it is important to consider spatial and temporal variability in suspended sediment sources in order to develop and target sediment control management strategies. The sources of suspended sediment and sediment deposited on the stream bed might not necessarily be the same. Therefore, sampling both suspended sediment and stream bed sediment will improve our knowledge of watershed-level sediment transport processes.

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Section: Comparison Between Suspended and Stream Bed Sediment Sourcesmentioning

confidence: 89%

Fingerprinting Suspended Sediment Sources in an Urbanized Watershed

Malhotra

Lamba

Srivastava

et al. 2018

Water

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“…1). The source and stream bed sediment samples were collected as part of the Malhotra et al (2020) study. During the study period, significant sediment mobilization from the construction sites that were not managed adequately was observed, which could have resulted in a substantial sediment contribution from these sites toward in-stream sediment (fig.…”

Section: Sampling Strategymentioning

confidence: 99%

“…3). Visual observations also indicated the existence of unstable and severely eroding stream banks marked by bare surfaces with little or no vegetation, exposed soils, and vertical walls that appeared to be susceptible to erosion (Malhotra et al, 2020) (fig. 3).…”

Section: Sampling Strategymentioning

confidence: 99%

“…The specific objectives of this study were to: (1) determine the relative contributions to stream bed sediment from construction sites and stream banks using two different machine learning techniques and a Bayesian mixing model; and (2) determine how the source apportionment results for the sediment deposited on the stream bed and associated uncertainty in this study compare with a previous study that employed a twostep statistical procedure (U-test + DFA) and a frequentist mixing model. This study builds on the previous sediment fingerprinting studies (Malhotra et al, 2018(Malhotra et al, , 2020 conducted by the authors in an urbanized watershed in the eastern part of Alabama, USA. The results of this study, in conjunction with previous studies conducted in this watershed, can demonstrate that machine learning methods can be employed in sediment fingerprinting and assess the uncertainty associated with the source apportionments in a better way.…”

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confidence: 94%

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Application of Sediment Fingerprinting to Apportion Sediment Sources: Using Machine Learning Models

Malhotra,

Zheng,

Abebe

et al. 2023

Journal of the ASABE

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Highlights Relative source contributions to stream bed sediment from construction sites and stream banks were quantified. Two machine-learning techniques were used to select composite fingerprinting properties. The MixSIR Bayesian model was employed for source apportionment. Statistical methods employed for fingerprinting properties selection have the potential to impact source apportionments. Management strategies to reduce sediment mobilization should be targeted depending on the dominant source of sediment in each sub-watershed. Abstract. Sediment fingerprinting is an extensively used approach for investigating sediment sources by linking in-stream sediment mixtures with watershed source materials. The overall goal of this research was to estimate the relative source contributions of stream banks and construction sites to the stream bed sediment in an urbanized watershed (Alabama, USA) using a fingerprinting technique established on composite fingerprints selected by two different machine learning techniques at a sub-watershed scale. The two statistical approaches employed to select the subset of fingerprinting properties were: (1) the Random Forest algorithm (RF) with Gini importance ranking of variables; and (2) logistic regression with the least absolute shrinkage and selection operator (LASSO). A Bayesian mixing model was then used to estimate the distribution of mixing proportions along with the associated uncertainty. The models were built based on the composite fingerprints selected using the two machine learning methods. Overall, using the subset of fingerprints selected by RF and LASSO, the relative contribution of stream banks ranged from 14±9% to 97±2% and from 24±18% to 94±5%, respectively, throughout the watershed. The stream bank contributions were compared with a previous study conducted in the watershed that utilized a two-step statistical procedure (which involved a Mann-Whitney U-test as the first step and discriminant function analysis (DFA) as the second step) to select the composite of fingerprinting properties and a frequentist mixing model to calculate the source apportionments. The relative contributions of stream banks to stream bed sediment in the previous study reported ranged from 9±8% to 100±1%. Therefore, the study demonstrated the dependence of source attributions on the statistical procedures used to select the optimum composite fingerprints for sediment fingerprinting applications. Furthermore, the results underscored the importance of using different mixing model structures to obtain reliable estimates of source contributions. Keywords: Least absolute shrinkage and selection operator (LASSO), MixSIR Bayesian model, Random Forest (RF), Statistical techniques.

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“…Traditional sediment tracing methods based on physical, chemical, biological, and composite fingerprinting have been used worldwide (Collins et al, 1997;Gibbs, 2008;Nosrati et al, 2011;Malhotra et al, 2020). However, some evidence suggests that these methods have some limitations in practical application, especially applying to large catchments with complex sources (Vale et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assembly process and source tracking of microbial communities in sediments of Dongting Lake

et al. 2023

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Soil erosion resulted in homogenization of bacterial communities in the watershed.• Microbial community heterogeneity among erosion sites made soil tracing possible.• Assembly process results showed that the tracking results can achieve high precision.• Dryland was the main source of sediment deposition based on the result of FEAST. Sediment source tracing can accurately provide a theoretical basis for controlling soil erosion effectively, by identifying the most serious types of land use. Traditional sediment tracing methods are based on physical, chemical, biological, and composite fingerprinting, which have not included microbes. As high-throughput sequencing becomes more prevalent, microorganisms can provide more information than what we think. Thus, whether the microorganism can also be used as a special fingerprint factor for sediment source identification during soil erosion, we have tested it by using microbial source tracking tool FEAST to quantify the microbe contribution from five types of eroded land (including dryland, urban, paddy field, forest and grassland) to the depositional areas (Niubitan) in the Yuanjiang basin. The source microbial community in the erosive area was heterogeneous, and assembly process analysis further demonstrated that the source tracking results could reach higher accuracy. The results of FEAST showed that dryland (35.50%), urban (17.21%), paddy field (8.14%), and forest (1.07%) were the major contributors to Niubitan. Our results follow the general soil erosion rules and prove its validity. Taken together, a new perspective is provided by these results for tracing sediment sources in erosion-sedimentary systems.

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Sources of stream bed sediment in an urbanized watershed

Cited by 15 publications

References 53 publications

Fingerprinting Suspended Sediment Sources in an Urbanized Watershed

Fingerprinting Suspended Sediment Sources in an Urbanized Watershed

Application of Sediment Fingerprinting to Apportion Sediment Sources: Using Machine Learning Models

Assembly process and source tracking of microbial communities in sediments of Dongting Lake

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