Influence of Composite Sediment fingerprinting in identifying the sources of suspended sediments of a semiarid reservoir

Influence of Composite Sediment fingerprinting in identifying the sources of suspended sediments of a semiarid reservoir | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Composite Sediment fingerprinting in identifying the sources of suspended sediments of a semiarid reservoir Jagannathan Shanmugam, Mathiazhagan Mookiah, Saravanan Karuppanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3905913/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sediment source fingerprinting is intended to provide the source of the sediment carried in fluvial systems. The suspended sediment load carried by a river or stream will be a mixture of sediment from many sources and sites within the contributing catchment. This study uses sediment fingerprinting approach to investigate the sources of sediments into the wellington reservoir of Tamilnadu, India. The sediment samples were analyzed for several potential fingerprint characteristics in the laboratory. By comparing the fingerprint of the sediment sources using multivariate mixing model, the contributions from each sources were ascertained. The uncertainty from the mixing model was quantified using Monte Carlo method. The sediment fingerprinting results indicate that Agriculture lands contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments. The montecarlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples. The results helps in understanding the potential regions of soil erosion that should be considered for watershed management against sedimentation. Chemical Analysis Reservoir Sedimentation Sediment Fingerprinting Soil Erosion Statistical tests Suspended sediments Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 2 Introduction Soil erosion has been widely recognized as one of the most important forms of land degradation across the world and is greatly influenced by land use/cover, soil types, climate and lithology (Fleskens and Stringer, 2014). Accelerated soil erosion and sediment transport pose extensive challenges to ecosystem services that is necessary for long-term sustainability and development (García-Ruiz et al., 2017). The eroded soils and contaminants often have a negative impacts on fresh water quality and aquatic habitats (Tiessen et al., 2010). Reliable Information on sediment sources in river catchments is necessary to develop well-designed policies and control measures for safeguarding soil and water resources. The Sediment fingerprinting approach has been increasingly utilized to assist managers in identifying sources of sediment in a watershed (Miller et al., 2015). Sediment fingerprinting procedures are employed to quantify sediment source contributions and have become a widely used tool to determine the sources of sediments. A specific physical, chemical, or biological feature of the soil or sediment is known as a natural tracer, often known as a sediment fingerprint (Walling, 2005). The 1970s is the beginning of the fingerprinting method for locating the sources of transported silt (Klages, 1975; Wall and Wilding, 1976). There is a vast body of literature all over the world that describes the technological development and application of sediment source fingerprinting (Liu et al., 2017; Ramarao et al., 2019). This method's fundamental premise is the collection of target sediment and catchment source material samples, followed by a comparison of their characteristics fingerprints to determine the relative significance of various upstream sources (Collins et al., 2020). Initially a single fingerprinting property was used to identify several sources of sediments; however it is soon recognized to be ineffective in discriminating the sources of sediments (Walling, 2005). The method of Composite fingerprinting with a number of tracer properties plays efficiently in identifying the potential sediment sources (Pulley et al., 2019). Tracer selection plays a critical role in sediment source fingerprinting, since different tracers may yield different proportional contributions (Laceby et al., 2019). As (Zhang and Liu, 2016) addressed, the nonparametric Kruskal–Wallis H test or Mann–Whitney U test can be used for tracer selection and the Discriminant function analysis or other multivariate variance analysis is applied to identify the optimum set of tracers. Then the linear mixing model is used to quantify the relative contributions from different sediment sources (Huang et al., 2015). In the process of sediment source apportionment, uncertainties associated with the source contributions have received increasing attention (Walling, 2013). Such uncertainties result mainly from measurement errors and the heterogeneity in sources and sediment (Stewart et al., 2015). To reduce uncertainty in sediment source apportionment, it is crucial to define sources and use appropriate tracers (Barthod et al., 2015). Uncertainty regarding the relative contributions of sources to sediment is expected to grow if the tracers cannot be used to distinguish between sources. (Stewart et al., 2015). Numerical methods such as Monte Carlo simulation and Bayesian uncertainty framework have been used to evaluate the degree of uncertainty in each source category (Koiter et al., 2013). The tracer-based fingerprinting methods can be used to enhance the knowledge of sediment sources in the watershed. Thus the objective of the study is to quantify the relative contribution of the potential sources using a sediment fingerprinting technique 3 Materials and Methods 3.1 Study area The Wellington reservoir is located between longitude 79° 20’ 19.31"E and 79° 60’ 54.84"E and latitude 11° 24’ 13.56"N and 11° 27’ 24.36"N as shown in Fig. 1 . The Reservoir is located in velar basin of Cuddalore District of Tamilnadu state, India as shown in Fig. 1 . The Vellar river rises about 900 m above sea level close to the village of Tumba in the Chittori hills of the Eastern. It runs through the Salem and Cuddalore districts of Tamil Nadu for 210 kilometers before it finally empties into the Bay of Bengal. Important right tributaries of the Vellar include Swetanadhi and Chinnar, and left tributaries include the Gomukinadhi and Manimukthanadhi. The entire basin covers an area of about 8,922 Sq.km. The Wellington is a rain-fed reservoir, with a total catchment area of about 129.50 Sq.km. The reservoir which has two rivulets that comes from Nangoor Reserve Forest and Lakkur Reserve Forest The Decadal rainfall analysis from 1911 to 2000 reveals that 17 districts of Tamilnadu has been shifted to semi arid zone from dry sub humid class and Cuddalore is one of the District (Shanmugam Panneerselvam, 2016). Also the Drought vulnerability analysis based on 120 years of Rainfall reveals that cuddalore falls under semi-arid region (Natarajan et al., 2023) The reservoir has a maximum water depth of 9.05 m, a Full Reservoir Level (FRL) of 72.540 m above mean sea level, and a gross storage capacity of 73.05 Mm 3 . This reservoir serves as the main irrigation source for more than 67 settlements and almost 25,000 acres of land. The major type of soil that constitutes the catchment is clay and sandy clay. They spread for around 81.8 Sq.km in agricultural fields of the catchment. The soil is excellent for growing primary crops including cotton, rice, sugarcane, groundnuts, and pulses. 3.2 Methodology The suspended sediment load carried by a river or stream will be a mixture of sediment from many sources and sites within the contributing catchment. The sources of sediments will be generated from difference land use and different erosion types. The sediments should be gathered such that they fully reflect a catchment's sources. Hence an initial reconnaissance survey across the study area was done to identify potential sources of sediments. Three potential sources of sediments namely Agricultural lands, Forests and Fallow lands were identified in the study area. Sediment fingerprinting studies often uses a different number of source samples (Charles M. Davis, 2009). Because of the robustness of fingerprinting techniques, the quantity of samples needed is a fundamental problem (Collins et al., 2017). There are 10 to 30 subsamples that can be obtained from each source, depending on the size of the source (Blake et al., 2018). As done by (Vale et al., 2016), sampling was carried out by scraping the top soil layer for a depth of between 2 and 8 cm. To discriminate between n sources with absolute certainty, at least n -1 properties must be present (Walling, 2013). Six samples from the reservoir and 15 samples from each source with a subsample of 12, were taken for this investigation as shown in Fig. 2 and Fig. 3 . The sediment sampling locations at source and in reservoir is given in Table 1 and Table 2 respectively Table 1 Coordinates of Sampling Locations at Source points Sampling Locations Latitude (N) Longitude (E) Forests Sample 1 11° 28' 27.34" 78° 57' 37.28" Sample 2 11° 28' 51.53" 78° 58' 31.13" Sample 3 11° 28' 2.37" 78° 59' 10.94" Sample 4 11° 28' 37.49" 79° 0' 7.91" Sample 5 11° 28' 54.66" 79° 2' 19.81" Agricultural Lands Sample 1 11° 27' 49.88" 79° 0' 31.32" Sample 2 11° 26' 45.88" 79° 0' 55.52" Stream 3 11° 25' 49.69" 79° 2' 17.46" Sample 4 11° 28' 2.37" 79° 2' 24.49" Sample 5 11° 27' 16.32" 79° 3' 7.41" Fallow Lands Sample 1 11° 25' 34.86" 79° 0' 51.61" Stream 2 11° 28' 2.37" 79° 1' 25.95" Sample 3 11° 26' 30.27" 79° 2' 35.41" Sample 4 11° 25' 37.98" 79° 4' 7.51" Sample 5 11° 27' 22.56" 79° 4' 56.68" Table 2 Coordinates of Sampling Locations within Wellington Reservoir Sampling Locations Latitude (N) Longitude (E) Sample 1 11° 26' 41.64" 79° 4' 30.36" Sample 2 11° 26' 56.76" 79° 5' 49.92" Sample 3 11° 26' 9.6" 79° 5' 52.44" Sample 4 11° 24' 52.56" 79° 4' 1.2" Sample 5 11° 26' 0.96" 79° 4' 53.76" Sample 6 11° 25' 4.44" 79° 5' 14.64" The entire soil samples collected were oven dried at 40°C and dry sieved using 63 µm sieve. For laboratory testing, the composite samples were divided using the coning and quartering procedure as shown in Fig. 4 . Based on the time and experimental resources that were available, fingerprint properties were chosen (Sadeghi et al., 2014). The number of sources determines how many properties are required to apportion the sediment (Liu et al., 2017). To differentiate between potential sediment sources, a variety of fingerprint properties will be used including physical, chemical, mineralogical, radiological, isotopic, and biological characteristics of sediments (Zhang & Liu, 2016). In this study properties like texture (Sand, Silt and Clay), heavy metals (Calcium, Magnesium, Potassium, Sodium, Copper, Nickel and Zinc), trace elements (Iron, Manganese and Aluminium), and organic characteristics (Carbon, Nitrogen and Phosphorous) were tested based on FAO and EPA methodologies, analysis of fingerprint attributes was conducted. The vast majority of studies continue to employ a straightforward screening method based on the so-called range or bracket test, employing a number of rules, to assess the conservative behavior of different tracers (Chambers et al., 2013). To demonstrate the substantial difference between each particular source type, the Kruskal-Wallis test is utilized (Walling, 2005b). To find the features that significantly discriminate between putative sources, the Kruskal-Wallis H test is performed (Walling, 2013). Eliminating any tracer that does not exhibit noticeable variations in concentrations between at least two sediment sources is also beneficial (Pulley et al. 2015). To the list of attributes chosen by the Kruskal-Wallis test, Discriminant Function Analysis (DFA) is used to examine tracers' capacity to identify sediment origins (Haddadchi et al., 2013). Discriminant Function Analysis was used to identify the ideal number of tracers (Palazón et al., 2015). A linear mixing model is used to quantify the relative contributions from different sediment sources (Du et al., 2016). These mixing models made up of a set of linear equations could be used to calculate the relative contributions from each source when a set of composite fingerprints made up of n suitable conservative tracers has been chosen to distinguish sources (Collins et al., 1997). The proportionate contribution of various sources is calculated by minimizing the mixing model's objective function's error difference (Laceby et al. 2019). Recent years have seen an increase in interest in the uncertainty issues related to sediment source fingerprinting study results. The mixing model optimization and the property values that are utilized to describe both sources and targets are significant sources of uncertainty. Because soil qualities are expected to vary regionally and may alter in response to the amount of erosion, source characterization, in particular, might involve significant uncertainties (Walling, 2013). To evaluate the degree of uncertainty surrounding the attributes in each source category, Monte Carlo simulation was employed (Malhotra et al., 2018; Mckinley et al., 2013). 4 Results and Discussion To find composite fingerprints that could distinguish between the samples taken to represent different source types upstream of each channel bed sampling site; a two-stage technique was applied. The Kruskal-Wallis H-test was used in stage one to verify the ability of individual fingerprint features to clearly identify source types. Larger test statistics are generated by properties that offer more contrasts between the various source types, and when these statistics exceed the critical value, Ho (the null hypothesis stating that measurements of the fingerprint property reveal no discernible differences between the source categories) is rejected. The SPSS software programme was used to perform the Kruskal-Wallis H-test. The second stage was reached by all fingerprint attributes that passed the Kruskal-Wallis H-test based on P-Value as shown in Table 3 . Table 3 Significance levels from the Kruskal-Wallis test Fingerprint tracer P-value Aluminium 0.206 Calcium 0.392 Clay 0.083 Copper* 0.005* Iron* 0.018* Magnesium 0.525 Manganese* 0.047* Nickel 0.368 Potassium* 0.018* Sand 0.534 Silt 0.797 Sodium* 0.004* Total organic carbon 0.051 Total Organic Nitrogen 0.585 Total Organic Phosphorous* 0.004* Zinc 0.766 In stage two, the best combination to categorize the source material is chosen from the attributes that passed stage one using multivariate Discriminant function analysis (DFA). Based on minimising the Wilks' lambda of those tracer features that passed the KW-H test, discriminant function analysis (DFA) was carried out. The final composite fingerprints with the maximum discriminating between sediment sources but with the fewest variables have been developed using DFA, which has been applied widely. A multivariate statistical test called Wilks' lambda evaluates the relevance of the model's discriminatory power. Wilks' lambda values range from 0 to 1. High discriminating power is shown by values around 0, whereas low discrimination is indicated by values near 1. The best composite fingerprint in the current investigation, as shown in Table 4 , Consists of a total of three distinct properties (copper, sodium and manganese). This fingerprint is utilized to define each of the source types and accurately differentiates 100% of the samples. Table 4 Discriminant Function analysis of fingerprint properties Fingerprint tracer Wilks lambda value Cumulative percentage of samples classified correctly Copper 0.021 77.8% Sodium 0.214 95.4% Manganese 0.651 100% The multivariate mixing model is based on a set of linear equations, where each selected tracer property has a corresponding equation that connects the tracer concentration in a sediment sample to the sum of the mean tracer concentrations for each source multiplied by the respective unknown proportional source contributions. An optimization approach is used to solve the mixing model by choosing values for Ps that minimize the sum of squares of the relative errors. The model is constrained by the requirements that proportional source contributions lie between 0 and 1 and the sum of all proportional source contributions should be equal to 1 which is a major constraint of the model. Since all the contributions gives 100% influence to the deposited sediments in the reservoir. The contribution of various sources is calculated using Eq. 1 using MS-Excel solver by minimizing the mixing model's objective function's error difference $${\sum _{\text{i}=1}^{\text{n}}\left[\text{C}\text{i}-\frac{\sum _{\text{i}=1}^{\text{m}}\text{P}\text{s} \text{S}\text{s}\text{i}}{\text{C}\text{i}}\right]}^{2}$$ 1 Ci = concentration of fingerprint property “i” in the target sample Ps = contribution from source category “s” (to be found) Ssi = Average concentration of fingerprint property “I” in the source sample The uncertainty produced by the mixing model was quantified using Monte Carlo method. Using a random number generator, cumulative Normal distributions were created based on the mean and standard deviation of the measurements for each fingerprint property for each source type. For a total of 1000 realizations, the mixing model was repeatedly solved by randomly selecting values from the corresponding Normal distributions for each property included in a composite fingerprint. Using the findings of the repeated iterations' standard error of the mean, 95 percent confidence limits for the relative contribution from each source type to each fine sediment sample were calculated. According to the findings, Agriculture land contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments as shown in Fig. 5 . The Monte Carlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples. 5 Conclusion Sediment-related environmental problems pose a serious threat to land management Sustainability and water resource utilization in many countries. But the efforts to conserve soil and water are frequently hampered by a complex interaction of physical, socioeconomic, political and technical factors. It is also frequently constrained by insufficient information on catchment sediment sources. This study investigated the use of fingerprint procedure in identifying the main sources of sediments for the wellington reservoir. The optimum composite fingerprint comprises of three individual properties (Copper, sodium and Manganese) correctly distinguishes sediment samples which is used to characterize each of the source types. The montecarlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples. The sediment fingerprinting results indicate that Agriculture lands contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments. The agricultural lands are often disturbed by practices like tillage which disturbs the upper soil layer and makes it more prone to soil erosion during intense precipitation. Thus the agricultural lands have more contribution in sources of sediments than the fallow lands and forests in the catchment. Preferences can be given to these agricultural lands in prioritizing the management methods to control inflow of sediments into the wellington reservoir. Declarations Acknowledgements Authors sincerely thank the Public Works Department and Wellington Reservoir managers to execute the research work Funding Not Applicable Conflict of interest/ Competing interests All authors declare that they have no conflict of interest/ competing interests Availability of data and material Not Applicable Authors Contribution Jagannathan Shanmugam: Conceptualization, Methodology. Mathiazhagan Mookiah: Field survey, Sample analysis, Results preparation. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3905913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269882924,"identity":"c9206b86-890f-471b-9992-c42110796e47","order_by":0,"name":"Jagannathan Shanmugam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACxgYowwBEJPywYWCQIFKLBFjLw540wlpgAKyF8QHbYcJamNuPX/xcuMOuzpz97AGGBJ7zif2zmw8+YKixicbpsJ6cYumZZ5IlLHvyEhgSLG4nzrhzLNmA4VhabgNOv+QkSPO2MUsYHMgxANpyO7HhRo6ZBGPDYdxa+t8k/+Ztq5cwOP8GqIXtXOJ8glpmpB8D2nJYwuAGyBa2A4kbCGt5w2bN23ZccsMNoC2JPcnGG2+kJRsk4PGLYX/649u8bdX8BudzDBh//LCTnXcj+eCDDzU2uLU08BjA2Ow/gIQjWGUCDuUgIM/A/gBFwB6P4lEwCkbBKBihAADih17HGAyNXwAAAABJRU5ErkJggg==","orcid":"","institution":"Anna University","correspondingAuthor":true,"prefix":"","firstName":"Jagannathan","middleName":"","lastName":"Shanmugam","suffix":""},{"id":269882925,"identity":"76d46428-360b-43b9-a603-ed5498d01e87","order_by":1,"name":"Mathiazhagan Mookiah","email":"","orcid":"","institution":"Anna University","correspondingAuthor":false,"prefix":"","firstName":"Mathiazhagan","middleName":"","lastName":"Mookiah","suffix":""},{"id":269882926,"identity":"629454b7-6ce7-442d-804d-2e5a1c405c24","order_by":2,"name":"Saravanan Karuppanan","email":"","orcid":"","institution":"Dhanalakshmi Srinivasan College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Saravanan","middleName":"","lastName":"Karuppanan","suffix":""}],"badges":[],"createdAt":"2024-01-28 13:46:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3905913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3905913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50420771,"identity":"14cc175c-ecca-4a94-a1a6-0b8b482ceec2","added_by":"auto","created_at":"2024-01-31 09:28:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation map of Wellington Reservoir\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/19395dbe5e05f49ca90752b9.jpeg"},{"id":50420773,"identity":"861bea57-c140-49bd-8ddb-b64964eac199","added_by":"auto","created_at":"2024-01-31 09:28:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40354,"visible":true,"origin":"","legend":"\u003cp\u003eSampling Locations at Sediment source point\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/957f2cd195ba6714ff3a7984.jpeg"},{"id":50420774,"identity":"706a2837-09db-4169-9bf2-b10b94037c82","added_by":"auto","created_at":"2024-01-31 09:28:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208865,"visible":true,"origin":"","legend":"\u003cp\u003eSampling Locations within Wellington Reservoir\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/aaccfcb927b524f88c144da9.jpeg"},{"id":50420775,"identity":"ec8622c9-9cb6-4444-8b89-d634b38d80ef","added_by":"auto","created_at":"2024-01-31 09:28:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":737208,"visible":true,"origin":"","legend":"\u003cp\u003eConing and Quartering procedure for Samples\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/41a2e13c56fb656c10c4044d.jpeg"},{"id":50421656,"identity":"505a6ce9-f4bf-4475-a09f-04d8fdf55aac","added_by":"auto","created_at":"2024-01-31 09:36:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContributions of sediments from each source\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/5c8b3a436b492392408f7f9e.png"},{"id":50692801,"identity":"4a8a4eb8-03d7-4159-baf3-357665ec34f7","added_by":"auto","created_at":"2024-02-05 22:22:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":666532,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3905913/v1/78edac66-cad9-4b92-ad28-1cb986357fcc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of Composite Sediment fingerprinting in identifying the sources of suspended sediments of a semiarid reservoir","fulltext":[{"header":"2 Introduction","content":"\u003cp\u003eSoil erosion has been widely recognized as one of the most important forms of land degradation across the world and is greatly influenced by land use/cover, soil types, climate and lithology (Fleskens and Stringer, 2014). Accelerated soil erosion and sediment transport pose extensive challenges to ecosystem services that is necessary for long-term sustainability and development (Garc\u0026iacute;a-Ruiz et al., 2017). The eroded soils and contaminants often have a negative impacts on fresh water quality and aquatic habitats (Tiessen et al., 2010). Reliable Information on sediment sources in river catchments is necessary to develop well-designed policies and control measures for safeguarding soil and water resources. The Sediment fingerprinting approach has been increasingly utilized to assist managers in identifying sources of sediment in a watershed (Miller et al., 2015). Sediment fingerprinting procedures are employed to quantify sediment source contributions and have become a widely used tool to determine the sources of sediments.\u003c/p\u003e \u003cp\u003eA specific physical, chemical, or biological feature of the soil or sediment is known as a natural tracer, often known as a sediment fingerprint (Walling, 2005). The 1970s is the beginning of the fingerprinting method for locating the sources of transported silt (Klages, 1975; Wall and Wilding, 1976). There is a vast body of literature all over the world that describes the technological development and application of sediment source fingerprinting (Liu et al., 2017; Ramarao et al., 2019). This method's fundamental premise is the collection of target sediment and catchment source material samples, followed by a comparison of their characteristics fingerprints to determine the relative significance of various upstream sources (Collins et al., 2020). Initially a single fingerprinting property was used to identify several sources of sediments; however it is soon recognized to be ineffective in discriminating the sources of sediments (Walling, 2005). The method of Composite fingerprinting with a number of tracer properties plays efficiently in identifying the potential sediment sources (Pulley et al., 2019).\u003c/p\u003e \u003cp\u003eTracer selection plays a critical role in sediment source fingerprinting, since different tracers may yield different proportional contributions (Laceby et al., 2019). As (Zhang and Liu, 2016) addressed, the nonparametric Kruskal\u0026ndash;Wallis H test or Mann\u0026ndash;Whitney U test can be used for tracer selection and the Discriminant function analysis or other multivariate variance analysis is applied to identify the optimum set of tracers. Then the linear mixing model is used to quantify the relative contributions from different sediment sources (Huang et al., 2015).\u003c/p\u003e \u003cp\u003eIn the process of sediment source apportionment, uncertainties associated with the source contributions have received increasing attention (Walling, 2013). Such uncertainties result mainly from measurement errors and the heterogeneity in sources and sediment (Stewart et al., 2015). To reduce uncertainty in sediment source apportionment, it is crucial to define sources and use appropriate tracers (Barthod et al., 2015). Uncertainty regarding the relative contributions of sources to sediment is expected to grow if the tracers cannot be used to distinguish between sources. (Stewart et al., 2015). Numerical methods such as Monte Carlo simulation and Bayesian uncertainty framework have been used to evaluate the degree of uncertainty in each source category (Koiter et al., 2013). The tracer-based fingerprinting methods can be used to enhance the knowledge of sediment sources in the watershed. Thus the objective of the study is to quantify the relative contribution of the potential sources using a sediment fingerprinting technique\u003c/p\u003e"},{"header":"3 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eStudy area\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe Wellington reservoir is located between longitude 79\u0026deg; 20\u0026rsquo; 19.31\"E and 79\u0026deg; 60\u0026rsquo; 54.84\"E and latitude 11\u0026deg; 24\u0026rsquo; 13.56\"N and 11\u0026deg; 27\u0026rsquo; 24.36\"N as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Reservoir is located in velar basin of Cuddalore District of Tamilnadu state, India as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Vellar river rises about 900 m above sea level close to the village of Tumba in the Chittori hills of the Eastern. It runs through the Salem and Cuddalore districts of Tamil Nadu for 210 kilometers before it finally empties into the Bay of Bengal. Important right tributaries of the Vellar include Swetanadhi and Chinnar, and left tributaries include the Gomukinadhi and Manimukthanadhi. The entire basin covers an area of about 8,922 Sq.km. The Wellington is a rain-fed reservoir, with a total catchment area of about 129.50 Sq.km. The reservoir which has two rivulets that comes from Nangoor Reserve Forest and Lakkur Reserve Forest\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Decadal rainfall analysis from 1911 to 2000 reveals that 17 districts of Tamilnadu has been shifted to semi arid zone from dry sub humid class and Cuddalore is one of the District (Shanmugam Panneerselvam, 2016). Also the Drought vulnerability analysis based on 120 years of Rainfall reveals that cuddalore falls under semi-arid region (Natarajan et al., 2023)\u003c/p\u003e \u003cp\u003eThe reservoir has a maximum water depth of 9.05 m, a Full Reservoir Level (FRL) of 72.540 m above mean sea level, and a gross storage capacity of 73.05 Mm\u003csup\u003e3\u003c/sup\u003e. This reservoir serves as the main irrigation source for more than 67 settlements and almost 25,000 acres of land. The major type of soil that constitutes the catchment is clay and sandy clay. They spread for around 81.8 Sq.km in agricultural fields of the catchment. The soil is excellent for growing primary crops including cotton, rice, sugarcane, groundnuts, and pulses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cem\u003eMethodology\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe suspended sediment load carried by a river or stream will be a mixture of sediment from many sources and sites within the contributing catchment. The sources of sediments will be generated from difference land use and different erosion types. The sediments should be gathered such that they fully reflect a catchment's sources. Hence an initial reconnaissance survey across the study area was done to identify potential sources of sediments. Three potential sources of sediments namely Agricultural lands, Forests and Fallow lands were identified in the study area.\u003c/p\u003e \u003cp\u003eSediment fingerprinting studies often uses a different number of source samples (Charles M. Davis, 2009). Because of the robustness of fingerprinting techniques, the quantity of samples needed is a fundamental problem (Collins et al., 2017). There are 10 to 30 subsamples that can be obtained from each source, depending on the size of the source (Blake et al., 2018). As done by (Vale et al., 2016), sampling was carried out by scraping the top soil layer for a depth of between 2 and 8 cm. To discriminate between n sources with absolute certainty, at least n -1 properties must be present (Walling, 2013). Six samples from the reservoir and 15 samples from each source with a subsample of 12, were taken for this investigation as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The sediment sampling locations at source and in reservoir is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoordinates of Sampling Locations at Source points\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling Locations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude (E)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 27.34\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u0026deg; 57' 37.28\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 51.53\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u0026deg; 58' 31.13\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 2.37\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u0026deg; 59' 10.94\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 37.49\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 0' 7.91\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 54.66\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 2' 19.81\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgricultural Lands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 27' 49.88\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 0' 31.32\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 45.88\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 0' 55.52\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStream 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 25' 49.69\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 2' 17.46\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 2.37\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 2' 24.49\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 27' 16.32\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 3' 7.41\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFallow Lands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 25' 34.86\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 0' 51.61\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStream 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 28' 2.37\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 1' 25.95\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 30.27\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 2' 35.41\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 25' 37.98\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 4' 7.51\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 27' 22.56\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 4' 56.68\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoordinates of Sampling Locations within Wellington Reservoir\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling Locations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude (E)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 41.64\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 4' 30.36\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 56.76\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 5' 49.92\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 9.6\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 5' 52.44\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 24' 52.56\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 4' 1.2\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 26' 0.96\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 4' 53.76\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 25' 4.44\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u0026deg; 5' 14.64\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe entire soil samples collected were oven dried at 40\u0026deg;C and dry sieved using 63 \u0026micro;m sieve. For laboratory testing, the composite samples were divided using the coning and quartering procedure as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the time and experimental resources that were available, fingerprint properties were chosen (Sadeghi et al., 2014). The number of sources determines how many properties are required to apportion the sediment (Liu et al., 2017). To differentiate between potential sediment sources, a variety of fingerprint properties will be used including physical, chemical, mineralogical, radiological, isotopic, and biological characteristics of sediments (Zhang \u0026amp; Liu, 2016). In this study properties like texture (Sand, Silt and Clay), heavy metals (Calcium, Magnesium, Potassium, Sodium, Copper, Nickel and Zinc), trace elements (Iron, Manganese and Aluminium), and organic characteristics (Carbon, Nitrogen and Phosphorous) were tested based on FAO and EPA methodologies, analysis of fingerprint attributes was conducted.\u003c/p\u003e \u003cp\u003eThe vast majority of studies continue to employ a straightforward screening method based on the so-called range or bracket test, employing a number of rules, to assess the conservative behavior of different tracers (Chambers et al., 2013). To demonstrate the substantial difference between each particular source type, the Kruskal-Wallis test is utilized (Walling, 2005b). To find the features that significantly discriminate between putative sources, the Kruskal-Wallis H test is performed (Walling, 2013). Eliminating any tracer that does not exhibit noticeable variations in concentrations between at least two sediment sources is also beneficial (Pulley et al. 2015). To the list of attributes chosen by the Kruskal-Wallis test, Discriminant Function Analysis (DFA) is used to examine tracers' capacity to identify sediment origins (Haddadchi et al., 2013). Discriminant Function Analysis was used to identify the ideal number of tracers (Palaz\u0026oacute;n et al., 2015).\u003c/p\u003e \u003cp\u003eA linear mixing model is used to quantify the relative contributions from different sediment sources (Du et al., 2016). These mixing models made up of a set of linear equations could be used to calculate the relative contributions from each source when a set of composite fingerprints made up of n suitable conservative tracers has been chosen to distinguish sources (Collins et al., 1997). The proportionate contribution of various sources is calculated by minimizing the mixing model's objective function's error difference (Laceby et al. 2019). Recent years have seen an increase in interest in the uncertainty issues related to sediment source fingerprinting study results.\u003c/p\u003e \u003cp\u003eThe mixing model optimization and the property values that are utilized to describe both sources and targets are significant sources of uncertainty. Because soil qualities are expected to vary regionally and may alter in response to the amount of erosion, source characterization, in particular, might involve significant uncertainties (Walling, 2013). To evaluate the degree of uncertainty surrounding the attributes in each source category, Monte Carlo simulation was employed (Malhotra et al., 2018; Mckinley et al., 2013).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cp\u003eTo find composite fingerprints that could distinguish between the samples taken to represent different source types upstream of each channel bed sampling site; a two-stage technique was applied. The Kruskal-Wallis H-test was used in stage one to verify the ability of individual fingerprint features to clearly identify source types. Larger test statistics are generated by properties that offer more contrasts between the various source types, and when these statistics exceed the critical value, Ho (the null hypothesis stating that measurements of the fingerprint property reveal no discernible differences between the source categories) is rejected. The SPSS software programme was used to perform the Kruskal-Wallis H-test. The second stage was reached by all fingerprint attributes that passed the Kruskal-Wallis H-test based on P-Value as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificance levels from the Kruskal-Wallis test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingerprint tracer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAluminium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNickel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal organic carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Organic Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Organic Phosphorous*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn stage two, the best combination to categorize the source material is chosen from the attributes that passed stage one using multivariate Discriminant function analysis (DFA). Based on minimising the Wilks' lambda of those tracer features that passed the KW-H test, discriminant function analysis (DFA) was carried out. The final composite fingerprints with the maximum discriminating between sediment sources but with the fewest variables have been developed using DFA, which has been applied widely. A multivariate statistical test called Wilks' lambda evaluates the relevance of the model's discriminatory power. Wilks' lambda values range from 0 to 1. High discriminating power is shown by values around 0, whereas low discrimination is indicated by values near 1. The best composite fingerprint in the current investigation, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Consists of a total of three distinct properties (copper, sodium and manganese). This fingerprint is utilized to define each of the source types and accurately differentiates 100% of the samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Function analysis of fingerprint properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingerprint tracer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilks lambda value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCumulative percentage of samples classified correctly\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe multivariate mixing model is based on a set of linear equations, where each selected tracer property has a corresponding equation that connects the tracer concentration in a sediment sample to the sum of the mean tracer concentrations for each source multiplied by the respective unknown proportional source contributions. An optimization approach is used to solve the mixing model by choosing values for Ps that minimize the sum of squares of the relative errors. The model is constrained by the requirements that proportional source contributions lie between 0 and 1 and the sum of all proportional source contributions should be equal to 1 which is a major constraint of the model. Since all the contributions gives 100% influence to the deposited sediments in the reservoir. The contribution of various sources is calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e using MS-Excel solver by minimizing the mixing model's objective function's error difference\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\sum _{\\text{i}=1}^{\\text{n}}\\left[\\text{C}\\text{i}-\\frac{\\sum _{\\text{i}=1}^{\\text{m}}\\text{P}\\text{s} \\text{S}\\text{s}\\text{i}}{\\text{C}\\text{i}}\\right]}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCi\u0026thinsp;=\u0026thinsp;concentration of fingerprint property \u0026ldquo;i\u0026rdquo; in the target sample\u003c/p\u003e \u003cp\u003ePs\u0026thinsp;=\u0026thinsp;contribution from source category \u0026ldquo;s\u0026rdquo; (to be found)\u003c/p\u003e \u003cp\u003eSsi\u0026thinsp;=\u0026thinsp;Average concentration of fingerprint property \u0026ldquo;I\u0026rdquo; in the source sample\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe uncertainty produced by the mixing model was quantified using Monte Carlo method. Using a random number generator, cumulative Normal distributions were created based on the mean and standard deviation of the measurements for each fingerprint property for each source type. For a total of 1000 realizations, the mixing model was repeatedly solved by randomly selecting values from the corresponding Normal distributions for each property included in a composite fingerprint. Using the findings of the repeated iterations' standard error of the mean, 95 percent confidence limits for the relative contribution from each source type to each fine sediment sample were calculated. According to the findings, Agriculture land contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The Monte Carlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eSediment-related environmental problems pose a serious threat to land management Sustainability and water resource utilization in many countries. But the efforts to conserve soil and water are frequently hampered by a complex interaction of physical, socioeconomic, political and technical factors. It is also frequently constrained by insufficient information on catchment sediment sources. This study investigated the use of fingerprint procedure in identifying the main sources of sediments for the wellington reservoir.\u003c/p\u003e \u003cp\u003eThe optimum composite fingerprint comprises of three individual properties (Copper, sodium and Manganese) correctly distinguishes sediment samples which is used to characterize each of the source types. The montecarlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples. The sediment fingerprinting results indicate that Agriculture lands contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments. The agricultural lands are often disturbed by practices like tillage which disturbs the upper soil layer and makes it more prone to soil erosion during intense precipitation. Thus the agricultural lands have more contribution in sources of sediments than the fallow lands and forests in the catchment. Preferences can be given to these agricultural lands in prioritizing the management methods to control inflow of sediments into the wellington reservoir.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors sincerely thank the Public Works Department and Wellington Reservoir managers to execute the research work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/ Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest/ competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJagannathan Shanmugam: Conceptualization, Methodology. 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Using multiple composite fingerprints to quantify fine sediment source contributions: A new direction. \u003cem\u003eGeoderma\u003c/em\u003e, \u003cem\u003e268\u003c/em\u003e(4), 108\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2016.01.031\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2016.01.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chemical Analysis, Reservoir Sedimentation, Sediment Fingerprinting, Soil Erosion, Statistical tests, Suspended sediments","lastPublishedDoi":"10.21203/rs.3.rs-3905913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3905913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSediment source fingerprinting is intended to provide the source of the sediment carried in fluvial systems. The suspended sediment load carried by a river or stream will be a mixture of sediment from many sources and sites within the contributing catchment. This study uses sediment fingerprinting approach to investigate the sources of sediments into the wellington reservoir of Tamilnadu, India. The sediment samples were analyzed for several potential fingerprint characteristics in the laboratory. By comparing the fingerprint of the sediment sources using multivariate mixing model, the contributions from each sources were ascertained. The uncertainty from the mixing model was quantified using Monte Carlo method. The sediment fingerprinting results indicate that Agriculture lands contributes higher amount of sediments 56.01%, followed by Fallow lands with 28.24% of sediments and Forests with 15.69% of sediments. The montecarlo uncertainty analysis reveals the average error of 2% in the suspended sediment samples. The results helps in understanding the potential regions of soil erosion that should be considered for watershed management against sedimentation.\u003c/p\u003e","manuscriptTitle":"Influence of Composite Sediment fingerprinting in identifying the sources of suspended sediments of a semiarid reservoir","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 09:28:49","doi":"10.21203/rs.3.rs-3905913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6b30a5a-f7b7-4745-b944-6ce8c494032c","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-05T22:14:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 09:28:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3905913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3905913","identity":"rs-3905913","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}