Enhancing sub-catchment sediment source fingerprinting using chemometric models for DRIFTS in different particle size subfractions.

Understanding the origins of sediment within stream networks is critical to developing effective strategies to mitigate sediment delivery and soil erosion in larger drainage basins. Sediment fingerprinting is a widely accepted approach to identifying sediment sources; however, it typically relies on labor-intensive and costly chemical analyses. Recent studies have recognized diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a non-destructive, cost-effective, and efficient alternative for estimating sediment contributions from multiple sources. This study aimed to assess (i) the effects of different particle size fractions on DRIFTS and conservatism tests, (ii) the effects of spectral pre-processing on discriminating sub-catchment spatial sediment sources, (iii) the efficiency of partial least squares regression (PLSR) and support vector machine regression (SVMR) chemometric models across different spectral resolutions and particle size fractions, and (iv) the quantification of sub-catchment spatial sediment source contributions using chemometric models across different particle size fractions. DRIFTS analysis was performed on three particle size fractions (<38 µm, 38-63 µm, and 63-125 µm) using 54 sediment samples from three different sub-catchments and 26 target sediment samples from the Andajrood catchment in Iran. Results showed significant effects of particle size fractions on DRIFTS for both sub-catchment sediment sources and target sediment samples. Conservatism tests indicated that DRIFTS behave conservative for the majority of target sediment samples. Spectral pre-processing techniques including SNV + SGD1 and SGD1 effectively discriminated sources across all particle size fractions and spectral resolutions. However, the optimal combination of pre-processing, spectral resolution, and regression models varied between sub-fractions. Validated model estimates revealed that sub-catchment 1 consistently contributed the most sediment across all particle size fractions, followed by sub-catchments 3 and 2. These results highlight the effectiveness of DRIFTS as a rapid, cost-effective, and precise method for discriminating and apportioning sediment sources within spatial sub-catchments.

Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments.

The identification of the spatial distribution of soil trace-elements and the contribution of different sources to the sediment yield is necessary for a better watershed and river water quality management. Until now, less attention has been paid to comprehensive assessments of sediment sources and soil trace-elements with respect to the suspended sediment production. The present study aimed at modelling the spatial distribution of soil trace-elements, quantifying the sediment sources apportionment and relating the landforms to polluted soils. Different techniques and approaches such as the Nemerow pollution index, machine learning algorithms (Random Forest (RF), generalised boosting methods (GBM), generalised linear models (GLM) and sediment fingerprinting were applied to the Kan watershed. A total of 79 soil samples having different Nemerow index values were considered for spatial modelling. Using statistical methods (Range test, Kruskal-Wallis and discrimination function analysis), an optimal set of tracers was selected. An unmixing model was applied to calculate the relative contribution of landforms for eight rainfall events. The results of the soil trace-element mapping showed that RF had the best performance with an accuracy of 83%. The evaluation of polluted soil areas showed that the landforms 'steep hills' and 'valley' contributed the most with 51% and 27% in the riparian zone, respectively. In addition, these landforms give a high contribution to sediment production in late-winter-spring events (29%) with a GOF (goodness of fit) of 0.65. The landform 'plain' had the highest contribution (28%) in sediment yield with a GOF of 0.72 in early-winter events. This means that the valley and steep hill landforms accelerate the transport of trace-elements across the watershed. Interestingly, the contribution of landforms varies during the year. Overall, the new proposed approach enables to better trace the origin of suspended sediments and trace-elements discharge into the river environment.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes.

PURPOSE: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. METHODS: Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. SCOPE: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. CONCLUSIONS: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

Using catchment characteristics to model seasonality of dissolved organic carbon fluxes in semi-arid mountainous headwaters.

Prediction of dissolved organic carbon (DOC) based on catchment characteristics is a useful tool for efficient and effective water management, but in the case of arid and semi-arid regions, such predictive capacity is scarce. Accordingly, the main objective of this study was to evaluate the significance of principal components for predicting DOC concentrations and fluxes in nine headwater catchments of the Hiv catchment located in the Southern Alborz Mountains in the west of Tehran, Iran. To achieve this aim, data were assembled on 24 headwater catchment characteristics comprising soil properties, physiography, seasonal rainfall, and flow attributes, as well as estimates of DOC concentrations and fluxes across four seasons. The results revealed a major positive correlation between DOC and soil organic matter parameters related to soil biological processes. Using general linear modelling, an organic matter component related to soil biology, a seasonal component related to the dummy effect of sampling seasons, and a soil physical component related to soil texture were found to be the best predictors for DOC responses in the study area.

Storm dust source fingerprinting for different particle size fractions using colour and magnetic susceptibility and a Bayesian un-mixing model.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-µm and 63-125-µm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal-Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63-125-µm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-µm fraction and from 1.8 to 5.8% for the 63-125-µm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-µm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63-125-µm fraction were 1.2-20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.

Fingerprinting sub-basin spatial suspended sediment sources by combining geochemical tracers and weathering indices.

Transport and deposition of fine-grained sediment, a pervasive nonpoint source pollutant, cause deleterious off-site impacts for water quality and aquatic ecosystems. Sediment fingerprinting provides one means of identifying the spatial sources of mobilised sediment delivered to fluvial systems in order to help target sediment control strategies and uptake of such source tracing procedures has been steadily increasing. Nonetheless, there remains a need to continue testing and comparing different composite signatures for source discrimination including the incorporation of physically grounded information relevant to erosion patterns. Accordingly, the objective of this study was to compare the discrimination and apportionment of sub-basin spatial suspended sediment sources in a mountainous basin in northern Tehran, Iran, using composite signatures comprising conventional geochemical tracers combined with lithological weathering indices or only the former. The list of conventional geochemical properties comprised Al, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, Sr, Ti, and Zn whilst three weathering indices were included: the chemical index of alteration (CIA), the weathering index of Parker (WIP), and the indicator of recycling (IR) which were all calculated based on elemental oxides. Using a composite signature combining conventional geochemical tracers and one weathering index (IR), the relative contributions from the sub-basin spatial sources were estimated at 1 (Imamzadeh Davood; 1.4%), 2 (Taloon; 13.4%), 3 (Soleghan; 35.9%), and 4 (Keshar; 48.4%) compared with corresponding respective estimates of 0.7%, 45.5%, 40.2%, and 13.3% using conventional geochemical tracers alone. Wald-Wolfowitz Runs test pairwise comparisons of the posterior distributions of predicted source proportions generated using the two different composite signatures confirmed statistically significant differences. These differing proportions demonstrated the sensitivity of predicted source apportionment to the inclusion or exclusion of a weathering index providing information reflecting the relative coverage of more erodible lithological formations in each of the sub-basins (32.7% sub-basin 1, 53.6% sub-basin 2, 58.5% sub-basin 3, and 63.2% sub-basin 4). The outputs of this study will be used to target sediment mitigation strategies.

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices.

STUDY REGION: The Kamish River catchment (308 km2); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran. STUDY FOCUS: The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and different weathering indices were measured or estimated for 43 tributary sediment samples collected to characterise three sub-basin spatial sediment sources and eleven target bed sediment samples collected at the outlet of the main basin. Statistical analysis was used to select three different composite signatures. NEW HYDROLOGICAL INSIGHTS FOR THE REGION: Using a composite signature based on KW-H and DFA, the respective relative contributions (with uncertainty ranges) from tributary sub-basins 1, 2 and 3 were estimated as 54.3% (47.8-62.0), 11.4% (4.2-18.7) and 34.3% (27.6-39.9), compared to 72.0% (61.6-82.7), 13.6% (9.0-18.5) and 14.2% (3.1-25.4) using a combination of KW-H and data mining, and 50.8% (42.8-59.9), 28.7% (20.2-37.3) and 20.3% (12.7-27.2) using a fingerprint selected by KW-H and PCCA. The root mean square difference between these source estimates highlighted sensitivity to the composite signatures. Evaluation of the un-mixing model predictions using virtual mixture tests confirmed agreement between modelled and known source proportions.

Investigating the importance of recreational roads as a sediment source in a mountainous catchment using a fingerprinting procedure with different multivariate statistical techniques and a Bayesian un-mixing model.

Road construction associated with land development generally increases erosion and sediment yields. Construction of unpaved roads has the potential to alter hydro-sedimentological behavior and catchment sediment source dynamics and, to date, this has largely been investigated in forested environments. The objective of this study, therefore, was to assess the relative importance of unpaved recreational roads as a sediment source alongside hillslope surface soils and stream channel banks in a non-forested mountainous catchment in northern Tehran, Iran, using a fingerprinting procedure. Eleven geochemical tracers were measured on 27 samples collected to characterise the sediment sources and five suspended sediment samples collected at the study catchment outlet. The statistical analysis employed to select three different composite fingerprints for discriminating the sediment sources comprised: (1) the Kruskal-Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), and (3) a combination of KW-H and principal components & classification analysis (PCCA). A Bayesian un-mixing model was used to ascribe sediment source contributions using the three composite fingerprints. Using the KW-H composite signature, the respective relative contributions (with uncertainty ranges) from recreational roads, hillslope surface soils and channel banks were estimated as 64.5% (57.7-73.1), 1.1% (0.1-4.9), and 33.9% (24.9-41.0), compared to 55.3% (45.5-68.5), 1.9% (0.1-7.9) and 42.1% (27.8-52.4) using a composite signature selected using a combination of KW-H and DFA, or 82.0% (69.7-93.8), 8.2% (0.7-22.7) and 7.3% (0.7-21.0) using a fingerprint selected using KW-H and PCCA. The root mean square difference between the apportionment results using the fingerprints identified on the basis of the three different statistical approaches ranged from 5.5% to 25.7%, highlighting the sensitivity of source estimates to the tracers used. Regardless, the different composite signatures all suggested that unpaved recreational roads were the dominant source of the suspended sediment samples, underscoring the need for mitigation measures targeting these anthropogenic features of the catchment system, including closure to permit re-vegetation, surface ripping and/or mulching to improve infiltration or gravel re-surfacing to reduce exposure of bare surfaces to sediment mobilisation.

Tracing sediment sources in a mountainous forest catchment under road construction in northern Iran: comparison of Bayesian and frequentist approaches.

Development and land use change lead to accelerated soil erosion as a serious environmental problem in river catchments in Iran. Reliable information about the sources of sediment in catchments is therefore necessary to design effective control strategies. This study used a composite sediment source tracing procedure to determine the importance of forest road cuttings as a sediment source in a mountainous catchment located in northern Iran. A fallout radionuclide (137Cs) and 12 geochemical tracers (Ca, Cu, Fe, K, Mg, Mn, Na, Ni, OC, Pb, Sr and TN) were used to determine the relative contributions of three sediment source types (hillslopes, road cuttings and channel banks) to both suspended and bed sediment samples. Two mixing models based on different mathematical concepts were used to apportion the sediment sources: the mixture sampling importance resampling Bayesian model which incorporates the mass-balance matrix and a distribution model using normal and summed probability of normal distributions. The results of both mixing models indicated that sub-soil erosion from road cuttings and channel banks dominated the sources of river bed and suspended sediment samples, respectively. These results therefore highlight that conservation that works in the study area to remedy the sediment problem should initially focus on stabilisation and rehabilitation of road cuttings and channel banks. This successful application of a composite (radionuclide and geochemical) tracing technique for discriminating source end members characterised by different erosion processes underscores the importance of sub-soil erosion in this case study.

Ascribing soil erosion of hillslope components to river sediment yield.

In recent decades, soil erosion has increased in catchments of Iran. It is, therefore, necessary to understand soil erosion processes and sources in order to mitigate this problem. Geomorphic landforms play an important role in influencing water erosion. Therefore, ascribing hillslope components soil erosion to river sediment yield could be useful for soil and sediment management in order to decrease the off-site effects related to downstream sedimentation areas. The main objectives of this study were to apply radionuclide tracers and soil organic carbon to determine relative contributions of hillslope component sediment sources in two land use types (forest and crop field) by using a Bayesian-mixing model, as well as to estimate the uncertainty in sediment fingerprinting in a mountainous catchment of western Iran. In this analysis, 137Cs, 40K, 238U, 226Ra, 232Th and soil organic carbon tracers were measured in 32 different sampling sites from four hillslope component sediment sources (summit, shoulder, backslope, and toeslope) in forested and crop fields along with six bed sediment samples at the downstream reach of the catchment. To quantify the sediment source proportions, the Bayesian mixing model was based on (1) primary sediment sources and (2) combined primary and secondary sediment sources. The results of both approaches indicated that erosion from crop field shoulder dominated the sources of river sediments. The estimated contribution of crop field shoulder for all river samples was 63.7% (32.4-79.8%) for primary sediment sources approach, and 67% (15.3%-81.7%) for the combined primary and secondary sources approach. The Bayesian mixing model, based on an optimum set of tracers, estimated that the highest contribution of soil erosion in crop field land use and shoulder-component landforms constituted the most important land-use factor. This technique could, therefore, be a useful tool for soil and sediment control management strategies.

Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques.

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.