Sediment quality evaluation utilizing hydrogeomorphological factors in northwestern Iran

Sediment quality evaluation utilizing hydrogeomorphological factors in northwestern Iran | 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 Sediment quality evaluation utilizing hydrogeomorphological factors in northwestern Iran Keivan Khorrami, Habib Nazarnejad, Ahmad Mahmoodzadeh, Farrokh Asadzadeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6561262/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Sep, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted 12 You are reading this latest preprint version Abstract Understanding the factors that influence sediment is vital for comprehending erosion and watershed conditions. Exploring the relationships and interactions between environmental and hydrogeomorphological factors and erosion and sediment processes is essential. In Ardabil province, northwest Iran, 98 sediment samples were collected from streambeds to investigate the relationship between hydrogeomorphological factors of watersheds and sediment quality. The integrated quality index (IQI) of sediments was calculated from their physicochemical characteristics, and its correlation with environmental factors was assessed using stepwise regression. The findings revealed an inverse relationship between Elevation and sediment quality index, with non-standard ß and standard ß coefficients of -0.000694 and − 0.393, respectively, significant at the p < 0.01 level. Vegetation cover and slope variables show a direct correlation with IQI at p < 0.05 level, with standard coefficients of + 0.001 and + 0.002, and unstandardized ß coefficients of + 0.235 and + 0.225. The IQI indicates that a value closer to one at sampling locations signifies better sediment quality, making these sites relevant for erosion and sediment management. The sediment quality index is a crucial measure for evaluating environmental conditions and land degradation, helping to prioritize areas for intervention. Therefore, identifying the environmental factors affecting sediment in each watershed is essential. Sediment quality index stepwise regression environmental factors soil physicochemical properties Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sediment control is a cornerstone of water and soil conservation and watershed management (Gajbhiye et al., 2013). Effective sediment management requires understanding the interplay between sediment formation and environmental, geomorphological, and physiographic factors (Keesstra et al., 2019 ). Evaluating Sediment quality, influenced by hydrogeomorphological characteristics, is vital for mitigating erosion and land degradation (López-Vicente et al., 2021 ). Sediments, comprising minerals, water, air, and biological components, vary spatially and temporally due to natural processes and human activities (Adesuyi et al., 2015; Montgomery et al., 2000 ). The minerals found in sediments, such as clay, silt, sand, and gravel (Gupte and Shaikh, 2014 ), come in various sizes and forms and are transported by natural forces (Montgomery et al., 2000 ; An et al., 2017 ; Zhou et al., 2024 ). Erosion, sedimentation, and soil nutrient depletion are driven by factors such as climate, elevation, lithology, land use, and anthropogenic impacts (Hevia et al., 2007 ; Symeonakis et al., 2007 ; Xu et al., 2011 ; Bajocco et al., 2012 ; Vanwalleghem et al., 2017 ; El Ouahabi et al., 2017 ; Huang et al., 2017 ; Rodrigo-Comino et al., 2018 ; Keesstra et al., 2019 ; Manojlović et al., 2022 ; Hegde et al., 2023 ). Erosion-derived sediment in a watershed highlights the link between geomorphological and environmental factors and the basin's sedimentological characteristics (Ahmadabadi et al., 2015 ; Sedighi et al., 2021 ). Understanding the factors influencing this process is essential for grasping the dynamics of erosion and sedimentation (Shayan et al., 2013 ; Choné & Biron, 2016 ; Baymanov & Baimanov, 2023 ). Sediment connectivity and runoff dynamics further complicate these processes (Cerdà et al., 2017 ; Rodrigo Comino et al., 2018 ). Numerous studies have explored how environmental and hydrogeomorphological factors affect sediment properties in watersheds, focusing on sediment production rates and quantitative characteristics of sediment. This study investigates the relationships between hydrogeomorphological factors and sediment quality in Ardabil province, northwest Iran, focusing on physicochemical properties such as organic matter, organic carbon, potassium, and soil texture, which influence soil stability and erosion resistance. Material and methods Study Area The study was conducted in Ardabil province, northwest Iran, spanning latitudes 37°45' to 39°42' N and longitudes 47°30' to 48°55' E. The region has an average elevation of 1,400 m above sea level, ranging from 100 m to 4,811 m (Fig. 1 ). Sample collection and determination of sampling points Ninety-eight sampling sites were selected based on basin accessibility and diversity in environmental and hydrogeomorphological factors (Fig. 1 ). Sediment samples were collected from streambeds at 0–5 cm depth using a shovel, and soil samples were taken at 20 cm depth with a 100 cm³ cylinder to measure bulk density. Sampling was less intensive in northern flat regions and central/southern mountainous areas due to uniform soil conditions and logistical constraints, respectively. Environmental factors included climatic (mean annual temperature and rainfall), geological (lithology), biological (vegetation density), and topographic (elevation, slope, aspect). Climatic data were sourced from 125 synoptic and rain gauge stations (15-year period) and 26 temperature stations. Geological data were derived from 1:100,000 maps digitized in ArcGIS 10.3, with erodibility assessed using Peyrowan et al. (2013). Vegetation density was quantified in 10 one-square-meter plots per site, and topographic parameters were measured using GPS, inclinometers, and azimuth meters. Physicochemical Analysis Sediment quality was evaluated using physicochemical parameters: soil texture (hydrometric method), exchangeable potassium (1N ammonium acetate), lime content (1N sodium hydroxide), pH (pH meter), electrical conductivity (EC meter, dS/m), bulk density (cylinder method), and organic carbon (Walkley-Black method). Sediment Quality Index The integrated quality index (IQI) was employed to assess sediment quality, serving as a comprehensive evaluation tool that integrates key physicochemical properties of the sediment. Given that these physicochemical characteristics can be measured and quantified, the IQI is equally applicable for determining sediment quality, providing a robust framework for evaluating its overall condition. The IQI was calculated using the Simple Additive method, integrating 10 physicochemical properties: organic carbon, organic matter, potassium, lime, pH, EC, sand, clay, silt, and bulk density. Variables were standardized (0–1 scale) using "more is better" (e.g., organic carbon, organic matter, potassium, and clay), "less is better", and "optimal range" (e.g., pH = 7, EC = 0.2–2 dS/m) scoring functions (Qi et al., 2009 ). "More is better" variables are those for which higher values correlate with improved sediment quality, while "less is better" variables are those for which lower values enhance quality (Liebig et al., 2001 ; Andrews et al., 2002 ; Marzaiolia et al., 2010. Using these methodologies, standardized and dimensionless scores were calculated for each variable, and the IQI was computed using Eq. 1. This index, representing the average of the standardized scores for all physicochemical properties of the sediments, was determined for each sediment sampling location across the basin. (Eq. 1) IQI = Σ(Si) /n S i = the standardized score for each Physicochemical variable; n = Number of physicochemical variables Statistical analysis Statistical analyses were conducted in three phases: Redundancy Analysis (RDA): RDA explored relationships between sediment physicochemical properties and hydrogeomorphological factors using CANOCO 5. Detrended Correspondence Analysis (DCA) confirmed RDA suitability (gradient length < 3). Stepwise Regression: SPSS 19 modeled the relationship between IQI and environmental factors, identifying significant predictors. T-test: Independent samples t-tests compared physicochemical properties of stream sediments and soil samples, with Levene’s test for variance equality (p < 0.05). RDA Analytical Method Redundancy analysis (RDA) is a powerful multivariate statistical method used in ecological and geomorphological research to explore the relationships between response and explanatory variables (Makarenkov and Legendre, 2002 ; Lepš and Šmilauer, 2003 ). It builds on multiple linear regression to identify linear relationships in data tables, often involving species or genetic information alongside environmental factors (Makarenkov and Legendre, 1999 ). However, since linear relationships may not fully represent ecological dynamics, polynomial RDA has been developed as a nonlinear alternative to capture more complex interactions (Makarenkov and Legendre, 2002 ). In landscape genomics, RDA serves as a versatile tool for modeling genomic variation in relation to environmental influences, effectively managing large datasets with numerous predictors (Capblancq and Forester, 2021 ). Its applications include variable selection, variance partitioning, genotype-environment relationships, and calculation of adaptive indices. RDA's ability to handle multivariate responses and explanatory variables makes it particularly suited for analyzing the complexities of natural systems (Capblancq and Forester, 2021 ). RDA is a direct gradient analysis technique that identifies linear relationships between response variables and independent variables using multiple linear regression and a matrix of fitted values (Ramette, 2007). The results are visualized as a biplot of the dataset. To examine the connections between sediment physicochemical properties and hydrogeomorphological factors in the watershed and perform multivariate and clustering analyses, the gradient length was calculated using Detrended Correspondence Analysis (DCA) with CANOCO 5 software. If the DCA gradient length is low (> 3), RDA is appropriate; otherwise, Canonical Correspondence Analysis (CCA) is recommended (Tian et al., 2012). The DCA analysis revealed a gradient length under three, leading to the selection of the RDA method for the statistical analyses of environmental and physicochemical data. A view of the output (biplot diagram) of the RDA model is shown in Fig. 2 . The relationship between enrichment ratios and integrated sediment quality index (IQI) with hydrogeomorphological factors of the basin was assessed using stepwise regression analysis using SPSS19 software. This method was employed to model the relationship between a dependent variable and multiple independent variables, as well as to identify which independent variables most effectively explain the variability in the dependent variable. The independent samples t-test was employed to analyze the physicochemical properties of stream sediments in relation to slope soils. Three categories were established: stream sediment, first hill slope soil, and second hill slope soil, comprising 98 total samples. The T-test in SPSS was used to compare groups based on the mean of ten physicochemical variables. Using Levene's test to check for variance equality, the t-test assessed mean differences at a significance level of 0.05. The higher and lower mean values for each group were identified and presented in tables and graphs. Results and Discussion Tables 1 and 2 summarize the hydrogeomorphological and physicochemical parameters, respectively, showing sufficient variability for robust statistical analysis. Table 1 Descriptive statistics of hydrogeomorphological parameters Row Environmental Parameter Minimum Maximum Mean Standard Deviation Coefficient of Variation (%) 1 Elevation (m) 173 2603 1299.11 503.05 38.72 2 Rainfall (mm) 222.54 403.87 316.44 48.96 15.47 3 Vegetation Cover (%) 2.25 84.5 31.10 19.25 61.9 4 Slope Direction 0.26 3.12 1.74 0.79 45.46 5 Temperature (°C) 4.71 15.54 10.99 2.19 19.97 6 Formation (unitless) 2 9 5.43 1.92 35.3 7 Stream Slope (%) 2 45 14.95 9.79 65.46 Table 2 Descriptive statistics of physicochemical parameters Row Physicochemical Parameter Minimum Maximum Mean Standard Deviation Coefficient of Variation (%) 1 Organic Carbon (%) 0.12 3.75 0.99 0.75 76.24 2 Organic Matter (%) 0.21 6.47 1.7 1.3 76.24 3 Acidity (unitless) 6.6 8.85 7.84 0.33 4.22 4 Lime (%) 0.5 23.5 8.46 4.78 56.41 5 Potassium (mg/kg) 3.79 78.16 25.33 17.52 69.16 6 Electrical Conductivity (dS/m) 0.002 1.99 0.32 0.39 112.16 7 Electrical Conductivity (dS/m) 0.64 2.06 1.39 0.29 20.63 8 Sand (%) 18.8 94.93 76.37 17.35 22.72 9 Clay (%) 2.54 48.3 9.66 7.91 81.87 10 Silt (%) 0.1 56.37 14.01 12.03 85.85 Based on the results presented in Tables (1) and (2), it is evident that most environmental and physicochemical variables exhibit a suitable range and variability, which is crucial for analyzing the relationship and impact of changes in environmental parameters on qualitative characteristics. This indicates that the favorable and diverse hydrogeomorphological variability predominates the research conditions, enabling a more effective explanation of changes in dependent parameters and their relationships with other variables. Additionally, this variability serves as a robust indicator for evaluating and analyzing the physiographic and sedimentological conditions at the study points within the examined basin. Results of statistical analysis The statistical evaluation results indicate an association between the basin's hydrogeomorphological factors and the physicochemical characteristics of sediment. Due to findings from the DCA (Table 3 ) and a gradient length of less than three, redundancy analysis (RDA) emerged as the most appropriate multivariate and linear classification model for examining the relationship between these factors in the study area and the sediment data. Table 3 Results of DCA Analysis Based on Four Axes Axis Cumulative Variance (%) Eigenvalue Gradient Length Axis 1 37.93 0.0173 0.54 Axis 2 60.38 0.0102 0.46 Axis 3 71.46 0.0051 0.37 Axis 4 80.40 0.0041 0.37 Table (3) shows a decreasing trend in eigenvalues and gradient lengths from the first to the fourth axis. The first axis notably explains changes in sediment physicochemical properties due to environmental factors. Given the gradient length (below 3), RDA analysis is a superior model for examining the relationship between hydrogeomorphological (explanatory or independent) and physicochemical (dependent or response) variables compared to Canonical Correspondence Analysis (CCA). Redundancy Analysis (RDA) effectively captures variations in dependent variables explained by independent variables, as summarized in Table (4). The higher eigenvalue and correlation emphasis of the first axis in the RDA table enhance its ability to clarify the association between these variables. Table 4 Results from Redundancy Analysis (RDA) on Environmental and Physicochemical Data Axis Eigenvalue cumulative variance explained (%) Focal correlation of response and explanatory variables Axis 1 0.1508 15.08 0.5971 Axis 2 0.0558 20.66 0.5391 Axis 3 0.0204 22.70 0.4528 Axis 4 0.0078 23.48 0.2913 Figure (3) presents the RDA model's graphical outcomes, illustrating the relationships between dependent physicochemical variables of sediment and independent hydrogeomorphological variables. In the RDA diagram, response and explanatory variables lack coefficients; their influence is indicated by the length of the arrows. Longer arrows signify greater effects (positive or negative), while shorter arrows indicate lesser effects. Aligned arrows represent a positive direct correlation, whereas non-aligned or opposing arrows (up to 180 degrees apart) indicate a negative reciprocal effect. Close variables that point in the same direction show a strong positive correlation, while those pointing in opposite directions demonstrate a significant negative correlation based on vector length. Vectors forming a right angle (90 degrees) indicate a lack of correlation. Overall, the RDA graphical output provides a comprehensive view of the interactions and relationships among variables, facilitating interpretation and analysis. Blue arrows denote response variables (sediment physicochemical properties), while red arrows represent independent variables (watershed hydrogeomorphological factors). RDA confirmed significant relationships between hydrogeomorphological factors and sediment properties (Fig. 2 , Table 4 ). Key findings include: - Organic carbon Sediment organic carbon Positively correlated with vegetation cover and slope, negatively with rock formation and aspect (Fig. 3 ). Vegetation cover has the strongest influence on organic carbon, followed by rock formation and stream direction (moderate impact), and slope (weakest impact). Other parameters show negligible correlation with organic carbon. The relationship between organic matter and hydrogeomorphological variables mirrors that of organic carbon. This study, consistent with previous research (Singh et al., 2003 ; Chen et al., 2005 ; Garcia, 2010; Nosrati, 2011 ; Wijitkosum, 2012 ; Alkharabshes et al., 2013; Pacheco et al., 2014 ; Zhang et al., 2018 ; Quijano et al., 2020 ; Wan et al., 2020 ; Yang et al., 2021 ), identifies vegetation cover as a key factor influencing sediment organic carbon, with higher vegetation cover correlating with increased organic carbon and organic matter. These studies highlight that different land uses and vegetation types vary in their capacity to enhance organic carbon, and that organic carbon retention decreases with decreasing vegetation cover. Sediment organic carbon decreases northward and increases southward. North-facing slopes, due to reduced sunlight exposure, exhibit higher moisture, deeper soils, greater fertility, and increased vegetation cover compared to south-facing slopes (Jendoubi et al., 2019 ). Consequently, they experience less erosion and sedimentation, minimizing organic matter and soil organic carbon loss to watersheds and streams, resulting in lower sediment organic carbon levels. Based on the findings of the RDA model, as rock formation sedimentation increases, sediment organic carbon decreases, influenced by slope aspect. Lozano- García et al. (2016) emphasize the importance of considering slope direction in organic carbon estimation models. Consistent with this, Yimer et al. ( 2006 ), Che et al. ( 2021 ), and Bagherifam et al. ( 2013 ) found that varying slope directions significantly alter organic carbon and nitrogen stocks due to microclimatic and vegetation differences. Stream slope significantly influences sediment organic carbon (SOC): increased slope correlates with increased SOC. While lower elevations can exhibit higher soil organic carbon, vegetation cover mediates the relationship between slope and SOC (Mohseni and Salar, 2021 ). Figure 3 shows that higher slopes generally have greater vegetation cover density, positively impacting SOC. As Battany and Grismer ( 2000 ) noted, vegetation cover and soil surface condition modify the impact of slope on runoff and sediment (Zhang et al., 2019 ). Sparse vegetation on steep slopes increases runoff and sediment yield. However, adequate vegetation density mitigates the increase in sediment production with increasing slope. Besides plant cover, slope orientation affects erosion and sedimentation (Sadeghi et al., 2011 ). While Jaksic et al. (2021) found slope alone doesn't determine organic carbon content, it's influenced by vegetation, land use, orientation, soil type, land management, altitude, and other factors. Smitha et al. (2002) and Tsui et al. ( 2004 ) observed higher organic carbon levels on ascending slopes, attributing this to increased vegetation accumulation, lower temperatures, and slower decomposition. Conversely, steeper slopes increase erosion and sediment transport, potentially enriching sediment with organic carbon. - Acidity (pH) Over 90% of samples were alkaline (mean pH 7.84). pH was positively correlated with rock formation and aspect, negatively with vegetation cover and slope. (Fig. 3 ). This correlation is stronger for vegetation cover and rock formation than for stream direction and slope. Other factors showed negligible or weak correlations, potentially due to near-vertical vectors. Increased erodibility correlates with higher, more alkaline sediment pH, confirming a positive relationship between pH and erosion intensity. This aligns with findings by Salmasi and Ahmadi ( 2012 ) who demonstrated that increased pH in marly terrains indicates greater erosion and can be used to classify erosion types. They suggested adjusting soil pH as an erosion control strategy. Matsumoto et al. ( 2018 ) attributed higher erosion rates at elevated pH levels to increased repulsive forces between soil particles. Research indicates that pH increases on north-facing slopes and decreases on south-facing slopes. However, given the weak negative correlation between pH and altitude, the relationship between pH and slope direction may vary across altitude classes, consistent with findings by Jolokhava et al. ( 2020 ) and Begum et al. ( 2010 ). In contrast, Tamene et al. ( 2020 ) found no clear relationship between acidity and slope direction. RDA modeling and previous studies (Moradi and Ahmadi-Pour, 2006; Heidarian Aghakhani et al., 2010 ) show a negative correlation between vegetation cover density and sediment pH. Reduced vegetation cover and organic matter lead to increased alkalinity and basic cations, which promote soil aggregate dispersion and erosion, consequently raising sediment pH. Conversely, increased vegetation cover and organic matter result in lower, more acidic pH levels. Boussaadi and Mouzai ( 2021 ) demonstrated a strong correlation between pH and species diversity in vegetation cover. Zaimes et al. ( 2017 ) observed that pH decreased with increasing vegetation cover density. RDA modeling further indicated that vegetation cover and formation erodibility significantly correlated with and explained pH levels. Higher pH increases alkaline cations like Na+, which can degrade soil particles, leading to erosion and acidic sediments. Conversely, Saeidian and Moradi ( 2013 ) found that in acidic soils (pH 4–7), increased aluminum ion activity contributes to soil particle accumulation. As pH increases, saturated aluminum decreases while basic cations with high swelling capacity increase. pH exhibits a negative correlation with stream slope percentage, consistent with Zare Mehrjerdi et al. ( 2007 ) and Karamian and Hosseini (2016). Because increased slope and pH accelerate erosion, especially in erosion-prone areas with alkaline tendencies, the influence of vegetation density and type, lithology, slope aspect and direction, altitude, and other environmental factors should be considered holistically. This integrated approach applies not only to slope but also to other parameters as revealed by the model. Slope percentage, like slope direction, influences acidity (pH), as determined by considering vegetation cover and geological unit erodibility. The consistently alkaline sediments (mean pH 7.84, CV 4.22) throughout the study area, coupled with the semi-arid climate, formations, and medium to poor rangelands with high grazing intensity, indicate high erodibility and sediment production potential in a significant portion of the sampling areas and sub-basins. - Electrical conductivity (EC) The RDA model (Fig. 3 ) indicates that EC Positively correlated with temperature and rock formation, negatively with precipitation and elevation. EC was generally low (< 4 dS/m), indicating non-saline sediments. Temperature and rock formation have a strong positive correlation with EC, while precipitation and altitude exhibit a strong negative correlation. Thus, EC increases with higher temperature and rock erodibility, and decreases with higher precipitation and altitude. EC shows little to no correlation with stream slope and vegetation cover. Consistent with Feyznia and Jafari ( 2002 ), this study found high EC in easily erodible evaporite marl sediments and low EC in less erodible igneous rocks. Asrari et al. (2012) found that electrical conductivity significantly influences plant community formation, which is often linked to temperature-driven evaporation concentrating soil salts and leading to high EC sediments. Elevated EC, particularly from sodium and ammonium, can exacerbate erosion due to soil swelling. Conversely, Smith et al. ( 2002 ) projected that increasing temperatures and decreasing precipitation would lower EC in steppe-shrub ecosystems. In arid and semi-arid regions, salt accumulation near the soil surface results in sediments with high electrical conductivity (EC) after erosion, potentially indicating increased erodibility. This aligns with research by Saeed et al. ( 2014 ) and Kanagaraj et al. ( 2017 ), which found a negative correlation between altitude and EC. Higher cation concentrations increase EC. Sediments in this study, predominantly below 4 ds/m, were classified as non-saline, with higher EC values observed in areas lacking vegetation and subjected to heavy grazing. - Potassium (K) Potassium (K) is vital for plant growth, yield, quality, and stress resistance (Lu et al., 2019 ; Romheld and Kirkby, 2010 ; Zorb et al., 2014 ), influencing soil erosion and sediment generation. Potassium Positively correlated with temperature, vegetation cover, and slope, negatively with elevation and precipitation (Fig. 3 ). Specifically, temperature, vegetation cover, rock formation, and slope show a positive correlation, while elevation, precipitation, and stream slope direction show a negative correlation. Temperature, vegetation cover, slope, direction, and rock formation are the strongest predictors of sediment potassium variation. Consistent with Keramati jobedar et al. (2013), this research also found highly variable mineral concentrations in areas with high temperatures and rainfall due to pronounced soil leaching, reducing calcium, magnesium, and potassium in plants. Similar to Poormirkamali and Mahmoodabadi ( 2021 ), we observed that increased precipitation intensity and wind lead to organic carbon loss and reduced potassium levels. Their finding of a positive correlation between vegetation cover and potassium levels, which aligns with our results, suggests that reduced vegetation density may contribute to lower potassium levels in soils and sediments. Annual potassium loss through leaching and erosion exceeds that of other elements, substantially reducing plant-available potassium. Increasing vegetation cover can mitigate erosion and sedimentation, addressing this complex issue. Varmaghany et al. ( 2007 ) found that altitude, precipitation, and temperature influence soil and plant mineral composition. Climate factors are the most significant variables affecting potassium levels (Li et al., 2021 ; Lybrand and Rasmussen, 2018 ; Mavris et al., 2015 ; Oborn et al., 2005 ). Altitude, influencing microclimate, can also affect soil formation and potassium concentration (Blanchet et al., 2017 ; Charan et al., 2013 ; Fang et al., 2019 ). Heavy rainfall leaches potassium (Francos et al., 2016 ), with leaching rates increasing in sediments, possibly due to elevation and vegetation cover. This study found that sediment potassium content rises with slope percentage, likely from erosion and subsequent enrichment, consistent with Rahimi Ashjerdi and Ayoubi ( 2014 ) who attributed this to clay movement and associated potassium particles on steep slopes. AL-Qahtani (1998) also found a positive correlation between slope length and potassium levels. North-facing slopes tend to have lower sediment potassium content, potentially due to improved moisture and vegetation, which reduce erosion compared to southern slopes. Hua et al. ( 2020 ) linked potassium levels to both slope direction and percentage. While vegetation correlates directly with potassium, other influencing parameters must also be considered. - Lime lime, positively correlated with slope, vegetation cover, and temperature, negatively with elevation and precipitation (Fig. 3 ). Kamali et al. (2011) found a significant relationship between lime percentage and formation erodibility, which varies across units due to other factors. Studies by Zare-Chahoki and Shafi-Zadeh, 2008 further indicate a strong link between vegetation cover and lime percentage. Lime content in soil and sediments varies with vegetation type. Zare Chahoki et al. ( 2016 ) identified it as a key factor differentiating vegetation types. Given lime's ability to neutralize acidic soils and the alkaline pH of sediments in the study areas, a significant relationship likely exists between vegetation cover and lime percentage in the region, as reflected in the sediments. Previous research supports this study's findings on the link between lime content and sedimentation. Castro and Logan ( 1991 ) demonstrated that lime, by increasing pH, promotes hydroxyl group ionization on clay surfaces, creating negative charges. Simultaneously, calcium cations facilitate clay aggregation and stability, reducing erosion. Vaezi et al. (2020) further emphasized a significant correlation between lime percentage and reduced soil erodibility, indicating that lime enhances aggregate stability and permeability, thereby decreasing erosion intensity and sediment yield. Similarly, Shourijeh et al. ( 2020 ) found that hydrated lime, rich in calcium, decreases erodibility by increasing critical shear stress and reducing lime in sediments. These results align with Costa et al. ( 2021 ), who observed reduced erodibility in lime-amended soils, and Chen et al. ( 2020 ), who reported enhanced clay compaction and decreased erodibility with increased calcium carbonate levels. In the sampled areas, increased precipitation correlates with a lower proportion of lime in sediments, likely due to increased vegetation cover, a more acidic environment, and reduced availability of alkaline cations like calcium. This reduction in lime, primarily calcium carbonate, can decrease lime enhancement of sediments. Given the low coefficient of variation and limited spatial variability of low precipitation in the study area, this relationship is likely influenced by interactions with other environmental and physicochemical variables (Mirhosseini et al., 2008 ). Conversely, lime positively correlates with coarse sand, organic matter, and soil permeability, reducing runoff (Vaezi and Haghani, 2020 ). This suggests lime plays a key role in regulating runoff in semi-arid soils, potentially reducing its presence in erosion sediments. Elevation is inversely correlated with lime and slope direction, consistent with Imran et al. ( 2021 )'s findings. However, the relationship between stream height and lime percentage differs between northern and southern slopes. Northern slopes, with higher moisture, vegetation cover, acidity, and reduced alkaline cations, exhibit lower lime levels than southern slopes. Finally, higher slope percentages correlate with a slight increase in lime in sediments, possibly due to increased erosion and reduced vegetation, supporting Zare-Khormizi et al. (2012)'s results. - Bulk Density Bulk density Positively correlated with precipitation, elevation, and vegetation cover, negatively with temperature and rock formation. Prior studies (Bahrami et al., 2014 ; Zhang et al., 2015 ) emphasize vegetation's influence on bulk density, porosity, and soil permeability. Crucially, plant species and root system characteristics should also be considered, as they affect pore density and porosity, thereby impacting bulk density. Mora and Lázaro ( 2014 ) corroborate this, noting bulk density variations among plant species. The study areas, characterized by diverse vegetation and moderate to heavy grazing, likely experienced soil compaction and increased bulk density. The RDA model reveals that increasing altitude correlates with decreased air temperature, negatively impacting microbial activity. Simultaneously, increased precipitation intensifies runoff and erosion, leading to greater soil and sediment compaction, reduced porosity and permeability, and thus, increased bulk density. Mohr et al. ( 2021 ) and Kavian et al. ( 2013 ) corroborate the positive relationship between bulk density and runoff. Given the average sediment bulk density of 1.3 g/cm³ at sampled locations, conditions are intermediate. Finer-textured areas exhibit lower specific gravity due to increased soil aggregates and pores, also reducing specific gravity through erosion. Therefore, assessing rock formation erodibility relative to sediment bulk density requires consideration of soil texture, structure, land use, other physicochemical characteristics, and organic matter. - Texture The analyzed sediment samples were primarily sandy, sandy-loamy, or loamy-sandy (light sediments), indicating similar light textures in adjacent slope soils (over 80% sand, under 20% silt and clay). This loose, fragile soil structure exhibits poor water retention and nutrient deficiencies, likely contributing to increased erosion and high sediment production. Compounding this issue, moderate to heavy livestock grazing and the inherent erodibility of the area's geological formations (sandstone, marl, conglomerate, and alluvial deposits, comprising about 60% of the area) necessitate soil enhancement and conservation measures. -sand This study reveals that sand content is positively correlated with altitude, rainfall, and slope direction, but negatively correlated with slope and temperature. Correlations with rock formation and vegetation cover are weaker due to other influencing factors. Consistent with Charan et al. ( 2013 ), a significant positive correlation between sand percentage and altitude was observed, although Saeed et al. ( 2014 ) found a negative correlation with altitude fluctuations. Increased sand content leads to coarser soil texture, reduced particle cohesion, and increased sand percentage through leaching. Smaller particles promote moisture retention, benefiting vegetation cover, though plant species also play a role. Chen et al. ( 2020 ) and Parsamehr et al. ( 2015 ) highlight the effectiveness of ground cover, like straw mulching, in managing and reducing coarse sediments such as sand. Kordian Hamedani et al. ( 2019 ) found that slope length and direction, rather than slope alone, influenced sediment production rates. Conversely, Vaezi and Ebadi ( 2017 ) observed that sand content decreased with increasing slope, while silt and clay remained unaffected. Therefore, considering both slope percentage and direction is beneficial when analyzing sediment particle size distribution. Increased soil temperature reduces moisture, potentially decreasing vegetation cover, loosening soil particle adhesion, and leading to a higher proportion of finer particles (clay) due to erosion and slope. Hydrogeomorphological parameters impact particle size distribution, with soil aggregate arrangement and particle structure also significantly influencing pore space and the relative amounts of sand, silt, and clay. -Silt and Clay Redundancy analysis (RDA) indicated that silt content is directly associated with slope, temperature, and vegetation cover, but inversely associated with slope direction, precipitation, and elevation. Silt's association with rock formation was weak. Clay content showed a positive correlation with temperature and rock formation, and a negative correlation with precipitation and elevation, with minimal association to slope direction, vegetation cover, and slope. The increased clay content observed with rising temperature and rock formation erodibility suggests that higher temperatures, promoting evaporation and reducing moisture, lead to looser soil aggregates and finer clay particles. This effect likely extends to silt as well. The RDA model further revealed that increased sedimentation is linked to higher clay content. Areas dominated by clay are generally less erodible (Rezaei, 2016 ), though the specific clay mineral composition is critical. Clays with high silica to iron/aluminum oxide ratios are prone to expansion upon wetting, resulting in unstable aggregates, whereas clays with lower ratios exhibit greater water resistance and reduced erodibility. Our findings align with Badia et al. (2016), who observed an inverse relationship between elevation and silt-sized particles. Furthermore, consistent with Vaezi et al. (2017), who documented a significant correlation between rainfall and sand, silt, and clay, this study also found a meaningful negative correlation between rainfall and both silt and clay, highlighting the importance of considering rainfall's impact on reducing silt and clay content in sediments in conjunction with vegetation cover and slope direction. Due to higher precipitation and vegetation on northern slopes, humus colloids bind clay particles, enhancing soil cohesion and reducing erosion, thus minimizing soil fragility and the formation of rills and slices. This is also beneficial for species cover. Soofi and Emami (2007) found that southern slopes exhibit greater erosion than northern slopes. Matus (2021) links organic matter content to the cycle and clay accumulation in sediments. Stepwise Regression Table (5) presents the results of the stepwise regression modeling process, which was conducted in three stages to derive the final optimal model, improving the relationship between variations in the dependent variable and the independent or explanatory variables. Table 5 Stepwise Regression Models For IQI Model Entered Variables Excluded Variables Method 1 Elevation (m) - Stepwise (Criterion: F probability for entry ≤ 0.05 and for removal ≥ 0.1) 2 Vegetation Cover (%) - Stepwise (Criterion: F probability for entry ≤ 0.05 and for removal ≥ 0.1) 3 Slope (%) - Stepwise (Criterion: F probability for entry ≤ 0.05 and for removal ≥ 0.1) In the IQI modeling process, at each stage, the variables demonstrating the strongest correlation with the dependent variable were retained. In the final step, three independent environmental variables -elevation (meters), vegetation density (percentage), and slope (percentage)- showed a stronger association with sediment quality compared to other factors and were more effective in explaining its variability or variance. Elevation showed a significant negative correlation (β = -0.393, p < 0.01), while vegetation cover and slope had positive correlations (β = 0.235 and 0.225, respectively, p < 0.05). Low collinearity (tolerance = 0.99, VIF ≈ 1) confirmed model robustness (Table 6 ). Table (6) presents the details and coefficients related to the stepwise regression modeling. Table 6 Coefficients and Results of Stepwise Regression Model Unstandardized Coefficients standardized Coefficients t Sig. Collinearity statistics B Std. Error β Tolerance VIF 1 Constant 0.505 0.023 21.525 0.000 Elevation (m) -6.2363E − 5 0.000 -0.355 -3.716 0.000 1.000 1.000 2 Constant) 0.474 0.026 18.418 0.000 Elevation (m) -6.630E − 5 0.000 -0.375 -4.035 0.000 0.993 1.007 Vegetation Cover 0.001 0.000 0.242 2.601 0.011 0.993 1.007 3 Constant 0.421 0.033 12.863 0.000 Elevation (m) -6.949E − 5 0.000 -0.393 -4.329 0.000 0.986 1.014 Vegetation Cover 0.001 0.000 0.235 2.595 0.011 0.992 1.008 Slope (%) 0.002 0.001 0.225 2.485 0.015 0.992 1.008 The elevation parameter exhibits a significant inverse correlation with the IQI, as evidenced by unstandardized (B = -0.000694) and standardized (β = -0.393) coefficients at a significance level of < 0.01. In contrast, vegetation cover and slope parameters demonstrate a positive correlation with IQI, with unstandardized coefficients (B) of + 0.001 and + 0.002, and standardized coefficients (β) of + 0.235 and + 0.225, respectively, at a significance level of < 0.05. Among these, the elevation parameter, with a β value of -0.393, has the most substantial influence on sediment quality, while the slope parameter, with a lower positive β value, shows a comparatively weaker correlation and impact relative to elevation and vegetation cover. The collinearity among the independent variables in this model is minimal, as indicated by tolerance coefficients of 0.99, demonstrating nearly no linear interdependence. This is further supported by the variance inflation factor (VIF), which confirms an extremely low level of collinearity among the hydrogeomorphological variables. The standard error of the final model underscores its effectiveness in predicting changes in the dependent variable (sediment quality) using the independent variables, as well as the robustness and suitability of the proposed model (Eq. 2). (Eq. 2) IQI = 0.421–0.0000694 Elevation (m) + 0.001 Vegetation Cover (%) + 0.002 Slope (%) Based on the results of the stepwise regression model and Table 6 , lower elevations are more effective in enhancing sediment quality, as indicated by the inverse relationship between elevation and IQI; an increase in elevation corresponds to a decline in sediment quality at the sampled stream locations. Conversely, higher vegetation cover density and greater slope percentages are directly correlated with an increase in the sediment quality index. The RDA model analysis (Fig. 3 ) reveals that vegetation cover is directly correlated with slope, while slope negatively correlates with altitude. Additionally, there is a weak inverse relationship between vegetation cover and altitude. Altitude may also influence soil formation by affecting the microclimate. Soil formation is slower at higher altitudes, such as mountain regions, due to lower temperatures and reduced microorganism activity. This results in sparser vegetation and more rocky formations like pebbles, boulders and rock mass. Evidence from the sampling locations shows that most vegetation is found at lower elevations, where the conditions of temperature and humidity are more favorable, along with the transport of fine sediments and micronutrients through weathering, erosion, and downstream accumulation. Vegetation cover density increased on both the southern highlands and northern lowlands, particularly on slopes of 10 to 30 percent. However, as slopes exceeded 30 percent, density decreased. This increase in vegetation cover was linked to higher levels of organic matter, organic carbon, and potassium, leading to sediments rich in organic content and an improved sediment quality index. The presence of humic colloids alongside clay colloids suggests the likelihood of finer-grained sediments. IQI comprises 10 physicochemical sediment characteristics, illustrating the relationship between these properties and hydrogeomorphological parameters. A higher IQI signifies better sediment quality, characterized by increased organic carbon and potassium content, finer grains, lower acidity and electrical conductivity, and optimal lime and bulk density levels. The integrated sediment quality index assesses these attributes collectively and interdependently. As a result, the sediment quality index can be utilized as a benchmark for environmental assessments, such as land degradation, in watersheds. IQI values ranged from 0.22 to 0.68, with a coefficient of variation of 21% (Fig. 4 ). Higher IQI values were associated with lower elevations, higher vegetation cover, and moderate slopes (10–30%). Figure 4 illustrates the changes in the integrated sediment quality index (IQI) at sampling locations, revealing variations and sinusoidal patterns in sediment quality throughout the study area. From sampling points 40 to 70, variations were significant, with the highest IQI of 0.68 at site 40 (south of the basin) and the lowest at site 50 (north of the basin). Analysis of organic matter reveals higher quantities at site 40, which also has a finer particle size distribution, at 79 percent, compared to 22 percent at site 50. Moreover, the average vegetation cover on the slopes aligns closely with changes in the sediment quality index. The coefficient of variation (CV) for the IQI is about 21 percent, indicating uniform sediment quality with minimal change across the sampled points in the watershed. Discussion Research on sediment quality has primarily focused on small watersheds, largely analyzing suspended sediments qualitatively. In contrast, this study provides a comprehensive assessment of sediment quality in Ardabil province, integrating 10 physicochemical properties into the IQI. Unlike previous studies focusing on suspended sediments or small watersheds (Amezcua-Allieri and González-Macías, 2002 ; Zhang et al., 2015 ), this research examines streambed sediments across a diverse region. The IQI effectively captures sediment quality variations, with higher values indicating finer textures, higher organic content, and lower EC and pH, consistent with findings in other regions (Kim et al., 2019 ; Abou El-Anwar et al., 2021 ). It also analyzed and statistically assessed the relationship between these parameters and the environmental factors of the watershed. Accordingly, sediment quality was assessed using a quality index based on physicochemical characteristics, and its relationship with environmental factors was evaluated. Research on sediment quality at the watershed scale (streams and slopes) has been limited thus far. The research area must encompass various environmental and hydrogeomorphological elements to examine their influence on sediment attributes. Ardabil province in northwestern Iran was selected as the study basin due to its diverse environmental conditions, ongoing erosion and sedimentation issues, and significant annual financial resources dedicated to managing these challenges and implementing water and soil conservation efforts. Amezcua Allieri and González-Macías (2002) assessed river sediment quality in an industrial region using the sediment quality index (SQI), which incorporates parameters such as organic matter, pH, nitrogen, phosphorus, cation exchange capacity, nickel, and vanadium. They found that the SQI is an effective tool for evaluating the area's environmental condition. Sediment quality in the Danube River Basin is significantly influenced by hydrogeomorphological factors like river width and depth, which affect the accumulation of hazardous substances (Šorša et al., 2022 ). Zhang et al. ( 2015 ) used a partial least squares regression model to examine how watershed geomorphic parameters—such as topographical features, shape, roughness ratio, and drainage network—affect sediment in China's Loess Plateau. The study found that watershed shape and roughness significantly influence sediment values. Kim et al. ( 2019 ) assessed river sediment health quality using nine indicators and found that geochemical methods enhance the understanding of how different elements affect sediment quality. Li et al. ( 2020 ) examined how changes in watershed characteristics affect the quantity and quality of sediment dissolved organic matter in Jiaozhou Bay, China. The study focused on land use, landscape patterns, and watershed attributes across three sub-watersheds. Findings indicated that the qualitative aspects of sediment dissolved organic matter were more influenced by the quantitative features of the sub-watersheds than by their quantity. This work provides new insights into sediment organic matter quality and quantity. Abou El-Anwar et al. ( 2021 ) assessed river sediment quality using the SQI index and emphasized the need for regular monitoring. Crane et al. ( 2021 ) developed an index for evaluating lake sediment quality in the USA, considering total organic carbon, particle size, metabolites, and organochlorines, and encouraged further field studies by other researchers. The negative correlation between elevation and IQI reflects reduced soil formation and vegetation at higher altitudes, leading to coarser, less nutrient-rich sediments. Vegetation cover and slope enhance sediment quality by increasing organic matter and stabilizing soil aggregates, aligning with studies by Vaezi et al. (2020) and Costa et al. ( 2021 ). The predominance of sandy textures and alkaline pH underscores the region’s high erodibility, exacerbated by grazing and erodible lithology (e.g., marl, sandstone). Limitations include the uneven sampling distribution due to logistical constraints and the focus on streambed sediments, which may not fully represent slope dynamics. Future research should include additional sub-basins and incorporate temporal variations in sediment quality. Conclusion Environmental factors significantly influence erosion, leading to the generation and accumulation of sediment. While numerous studies have focused on sediment volume, this research specifically examines sediment quality and its relationship with hydrogeomorphological elements. The findings indicate that the variability in hydrogeomorphological parameters and the physicochemical characteristics of sediments and soils in the study area are generally acceptable. This data diversity enhances the reliability of the statistical analysis. A key achievement of the study is the development of a sediment quality index for the watershed, which evaluates locations based on a combination of physicochemical attributes related to erosion and sediment. Areas with higher sediment quality index values indicate better sediment quality and are prioritized for erosion and sediment control, especially where sediment volume and erosion rates are also elevated. This study highlights the significant influence of hydrogeomorphological factors on sediment quality in Ardabil province. The IQI, integrating 10 physicochemical properties, is a robust tool for assessing sediment quality and prioritizing erosion control measures. Lower elevations, higher vegetation cover, and moderate slopes are associated with better sediment quality, guiding targeted conservation strategies. Enhancing vegetation cover with adapted plant species can improve soil organic content and reduce erosion. The IQI serves as a valuable metric for environmental monitoring and land degradation assessment, with applications for watershed management and soil enhancement in degraded areas. Identifying the factors affecting sediment quality enables the development of targeted plans to improve conditions in specific regions or basins. One key action is to enhance vegetation cover with compatible plant species that can significantly increase organic material and humus in the soil, while considering the site's topography, such as slope, orientation, and altitude. The sediment quality index indicates areas with higher values represent better sediment quality and should be prioritized in planning. Additionally, sediments from these areas can be used to enhance low-quality and unproductive lands and pastures. The sediment quality index is a crucial indicator for evaluating environmental conditions, land degradation, and prioritizing areas for intervention. Therefore, identifying the environmental factors that affect it in each watershed is vital. This research showed that most study locations displayed similar variability patterns in sediment quality. Future studies should examine more key areas within the province and its sub-basins. Declarations Author Contribution Keyvan Khorrami and Habib Nazarnejad wrote the original text of the article. Sediment sampling was carried out by these two individuals.Ahmad Mahmoodzadeh contributed to the selection of sediment sampling sites and geographic data.Esmaeil Sheidai-Karkaj and Farrokh Asadzadeh contributed to the analysis of samples in the laboratory and statistical analysis. Artemi Sarda contributed to the statistical analysis of samples and final editing of the text. References Abou El-Anwar, E., Salman, S., Asmoay, A., Elnazer, A. (2021). Geochemical, mineralogical and pollution assessment of River Nile sediments at Assiut Governorate, Egypt. 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09:35:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31117,"visible":true,"origin":"","legend":"\u003cp\u003eRDA biplot showing relationships between physicochemical and hydrogeomorphological variables\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6561262/v1/d5b3e69345ebe7663b66e180.jpg"},{"id":84057220,"identity":"4f71dbe6-7c0d-4352-87f2-d56e4564728d","added_by":"auto","created_at":"2025-06-06 09:35:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41458,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the relationship between sediment physicochemical variables and hydrogeomorphological variables (RDA model)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6561262/v1/964174f67d4e0f7ae4341aee.jpg"},{"id":84057225,"identity":"da94aeae-13af-4ed5-93ec-d2423866068a","added_by":"auto","created_at":"2025-06-06 09:35:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33942,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in IQI across sampling points\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6561262/v1/ec5681139dc307faee1425d6.jpg"},{"id":92430917,"identity":"2147a3cd-0d05-4829-8947-4b9cc2ec5f65","added_by":"auto","created_at":"2025-09-29 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1315029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6561262/v1/f48b93de-c43a-4b96-a58c-b1e8e7afeb9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sediment quality evaluation utilizing hydrogeomorphological factors in northwestern Iran","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSediment control is a cornerstone of water and soil conservation and watershed management (Gajbhiye et al., 2013). Effective sediment management requires understanding the interplay between sediment formation and environmental, geomorphological, and physiographic factors (Keesstra et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvaluating Sediment quality, influenced by hydrogeomorphological characteristics, is vital for mitigating erosion and land degradation (L\u0026oacute;pez-Vicente et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sediments, comprising minerals, water, air, and biological components, vary spatially and temporally due to natural processes and human activities (Adesuyi et al., 2015; Montgomery et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe minerals found in sediments, such as clay, silt, sand, and gravel (Gupte and Shaikh, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), come in various sizes and forms and are transported by natural forces (Montgomery et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; An et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eErosion, sedimentation, and soil nutrient depletion are driven by factors such as climate, elevation, lithology, land use, and anthropogenic impacts (Hevia et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Symeonakis et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bajocco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vanwalleghem et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; El Ouahabi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodrigo-Comino et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Keesstra et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manojlović et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hegde et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eErosion-derived sediment in a watershed highlights the link between geomorphological and environmental factors and the basin's sedimentological characteristics (Ahmadabadi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sedighi et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding the factors influencing this process is essential for grasping the dynamics of erosion and sedimentation (Shayan et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chon\u0026eacute; \u0026amp; Biron, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Baymanov \u0026amp; Baimanov, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sediment connectivity and runoff dynamics further complicate these processes (Cerd\u0026agrave; et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodrigo Comino et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have explored how environmental and hydrogeomorphological factors affect sediment properties in watersheds, focusing on sediment production rates and quantitative characteristics of sediment. This study investigates the relationships between hydrogeomorphological factors and sediment quality in Ardabil province, northwest Iran, focusing on physicochemical properties such as organic matter, organic carbon, potassium, and soil texture, which influence soil stability and erosion resistance.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in Ardabil province, northwest Iran, spanning latitudes 37\u0026deg;45' to 39\u0026deg;42' N and longitudes 47\u0026deg;30' to 48\u0026deg;55' E. The region has an average elevation of 1,400 m above sea level, ranging from 100 m to 4,811 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and determination of sampling points\u003c/h3\u003e\n\u003cp\u003eNinety-eight sampling sites were selected based on basin accessibility and diversity in environmental and hydrogeomorphological factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sediment samples were collected from streambeds at 0\u0026ndash;5 cm depth using a shovel, and soil samples were taken at 20 cm depth with a 100 cm\u0026sup3; cylinder to measure bulk density. Sampling was less intensive in northern flat regions and central/southern mountainous areas due to uniform soil conditions and logistical constraints, respectively.\u003c/p\u003e \u003cp\u003eEnvironmental factors included climatic (mean annual temperature and rainfall), geological (lithology), biological (vegetation density), and topographic (elevation, slope, aspect). Climatic data were sourced from 125 synoptic and rain gauge stations (15-year period) and 26 temperature stations. Geological data were derived from 1:100,000 maps digitized in ArcGIS 10.3, with erodibility assessed using Peyrowan et al. (2013). Vegetation density was quantified in 10 one-square-meter plots per site, and topographic parameters were measured using GPS, inclinometers, and azimuth meters.\u003c/p\u003e\n\u003ch3\u003ePhysicochemical Analysis\u003c/h3\u003e\n\u003cp\u003eSediment quality was evaluated using physicochemical parameters: soil texture (hydrometric method), exchangeable potassium (1N ammonium acetate), lime content (1N sodium hydroxide), pH (pH meter), electrical conductivity (EC meter, dS/m), bulk density (cylinder method), and organic carbon (Walkley-Black method).\u003c/p\u003e\n\u003ch3\u003eSediment Quality Index\u003c/h3\u003e\n\u003cp\u003eThe integrated quality index (IQI) was employed to assess sediment quality, serving as a comprehensive evaluation tool that integrates key physicochemical properties of the sediment. Given that these physicochemical characteristics can be measured and quantified, the IQI is equally applicable for determining sediment quality, providing a robust framework for evaluating its overall condition.\u003c/p\u003e \u003cp\u003eThe IQI was calculated using the Simple Additive method, integrating 10 physicochemical properties: organic carbon, organic matter, potassium, lime, pH, EC, sand, clay, silt, and bulk density. Variables were standardized (0\u0026ndash;1 scale) using \"more is better\" (e.g., organic carbon, organic matter, potassium, and clay), \"less is better\", and \"optimal range\" (e.g., pH\u0026thinsp;=\u0026thinsp;7, EC\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;2 dS/m) scoring functions (Qi et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). \"More is better\" variables are those for which higher values correlate with improved sediment quality, while \"less is better\" variables are those for which lower values enhance quality (Liebig et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Andrews et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Marzaiolia et al., 2010.\u003c/p\u003e \u003cp\u003eUsing these methodologies, standardized and dimensionless scores were calculated for each variable, and the IQI was computed using Eq.\u0026nbsp;1. This index, representing the average of the standardized scores for all physicochemical properties of the sediments, was determined for each sediment sampling location across the basin.\u003c/p\u003e \u003cp\u003e(Eq.\u0026nbsp;1) IQI\u0026thinsp;=\u0026thinsp;Σ(Si) /n\u003c/p\u003e \u003cp\u003eS\u003csub\u003ei\u003c/sub\u003e= the standardized score for each Physicochemical variable; n\u0026thinsp;=\u0026thinsp;Number of physicochemical variables\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted in three phases:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRedundancy Analysis (RDA): RDA explored relationships between sediment physicochemical properties and hydrogeomorphological factors using CANOCO 5. Detrended Correspondence Analysis (DCA) confirmed RDA suitability (gradient length\u0026thinsp;\u0026lt;\u0026thinsp;3).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStepwise Regression: SPSS 19 modeled the relationship between IQI and environmental factors, identifying significant predictors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eT-test: Independent samples t-tests compared physicochemical properties of stream sediments and soil samples, with Levene\u0026rsquo;s test for variance equality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRDA Analytical Method\u003c/h2\u003e \u003cp\u003eRedundancy analysis (RDA) is a powerful multivariate statistical method used in ecological and geomorphological research to explore the relationships between response and explanatory variables (Makarenkov and Legendre, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lepš and Šmilauer, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It builds on multiple linear regression to identify linear relationships in data tables, often involving species or genetic information alongside environmental factors (Makarenkov and Legendre, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, since linear relationships may not fully represent ecological dynamics, polynomial RDA has been developed as a nonlinear alternative to capture more complex interactions (Makarenkov and Legendre, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In landscape genomics, RDA serves as a versatile tool for modeling genomic variation in relation to environmental influences, effectively managing large datasets with numerous predictors (Capblancq and Forester, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its applications include variable selection, variance partitioning, genotype-environment relationships, and calculation of adaptive indices. RDA's ability to handle multivariate responses and explanatory variables makes it particularly suited for analyzing the complexities of natural systems (Capblancq and Forester, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). RDA is a direct gradient analysis technique that identifies linear relationships between response variables and independent variables using multiple linear regression and a matrix of fitted values (Ramette, 2007). The results are visualized as a biplot of the dataset.\u003c/p\u003e \u003cp\u003eTo examine the connections between sediment physicochemical properties and hydrogeomorphological factors in the watershed and perform multivariate and clustering analyses, the gradient length was calculated using Detrended Correspondence Analysis (DCA) with CANOCO 5 software. If the DCA gradient length is low (\u0026gt;\u0026thinsp;3), RDA is appropriate; otherwise, Canonical Correspondence Analysis (CCA) is recommended (Tian et al., 2012). The DCA analysis revealed a gradient length under three, leading to the selection of the RDA method for the statistical analyses of environmental and physicochemical data.\u003c/p\u003e \u003cp\u003eA view of the output (biplot diagram) of the RDA model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationship between enrichment ratios and integrated sediment quality index (IQI) with hydrogeomorphological factors of the basin was assessed using stepwise regression analysis using SPSS19 software. This method was employed to model the relationship between a dependent variable and multiple independent variables, as well as to identify which independent variables most effectively explain the variability in the dependent variable.\u003c/p\u003e \u003cp\u003eThe independent samples t-test was employed to analyze the physicochemical properties of stream sediments in relation to slope soils. Three categories were established: stream sediment, first hill slope soil, and second hill slope soil, comprising 98 total samples. The T-test in SPSS was used to compare groups based on the mean of ten physicochemical variables. Using Levene's test to check for variance equality, the t-test assessed mean differences at a significance level of 0.05. The higher and lower mean values for each group were identified and presented in tables and graphs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the hydrogeomorphological and physicochemical parameters, respectively, showing sufficient variability for robust statistical analysis.\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\u003eDescriptive statistics of hydrogeomorphological parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient of Variation (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1299.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e503.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e403.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e316.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope Direction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormation (unitless)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream Slope (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.46\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 \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\u003eDescriptive statistics of physicochemical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysicochemical Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient of Variation (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic Carbon (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic Matter (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcidity (unitless)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLime (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotassium (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical Conductivity (dS/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical Conductivity (dS/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSand (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClay (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilt (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85.85\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\u003eBased on the results presented in Tables\u0026nbsp;(1) and (2), it is evident that most environmental and physicochemical variables exhibit a suitable range and variability, which is crucial for analyzing the relationship and impact of changes in environmental parameters on qualitative characteristics. This indicates that the favorable and diverse hydrogeomorphological variability predominates the research conditions, enabling a more effective explanation of changes in dependent parameters and their relationships with other variables. Additionally, this variability serves as a robust indicator for evaluating and analyzing the physiographic and sedimentological conditions at the study points within the examined basin.\u003c/p\u003e\n\u003ch3\u003eResults of statistical analysis\u003c/h3\u003e\n\u003cp\u003eThe statistical evaluation results indicate an association between the basin's hydrogeomorphological factors and the physicochemical characteristics of sediment. Due to findings from the DCA (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and a gradient length of less than three, redundancy analysis (RDA) emerged as the most appropriate multivariate and linear classification model for examining the relationship between these factors in the study area and the sediment data.\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\u003eResults of DCA Analysis Based on Four Axes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative Variance (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGradient Length\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\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\u003eTable\u0026nbsp;(3) shows a decreasing trend in eigenvalues and gradient lengths from the first to the fourth axis. The first axis notably explains changes in sediment physicochemical properties due to environmental factors. Given the gradient length (below 3), RDA analysis is a superior model for examining the relationship between hydrogeomorphological (explanatory or independent) and physicochemical (dependent or response) variables compared to Canonical Correspondence Analysis (CCA). Redundancy Analysis (RDA) effectively captures variations in dependent variables explained by independent variables, as summarized in Table\u0026nbsp;(4). The higher eigenvalue and correlation emphasis of the first axis in the RDA table enhance its ability to clarify the association between these variables.\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\u003eResults from Redundancy Analysis (RDA) on Environmental and Physicochemical Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecumulative variance explained (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal correlation of response and explanatory variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2913\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\u003eFigure (3) presents the RDA model's graphical outcomes, illustrating the relationships between dependent physicochemical variables of sediment and independent hydrogeomorphological variables. In the RDA diagram, response and explanatory variables lack coefficients; their influence is indicated by the length of the arrows. Longer arrows signify greater effects (positive or negative), while shorter arrows indicate lesser effects. Aligned arrows represent a positive direct correlation, whereas non-aligned or opposing arrows (up to 180 degrees apart) indicate a negative reciprocal effect. Close variables that point in the same direction show a strong positive correlation, while those pointing in opposite directions demonstrate a significant negative correlation based on vector length. Vectors forming a right angle (90 degrees) indicate a lack of correlation. Overall, the RDA graphical output provides a comprehensive view of the interactions and relationships among variables, facilitating interpretation and analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBlue arrows denote response variables (sediment physicochemical properties), while red arrows represent independent variables (watershed hydrogeomorphological factors).\u003c/p\u003e \u003cp\u003eRDA confirmed significant relationships between hydrogeomorphological factors and sediment properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Key findings include:\u003c/p\u003e \u003cp\u003e- \u003cb\u003eOrganic carbon\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSediment organic carbon Positively correlated with vegetation cover and slope, negatively with rock formation and aspect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Vegetation cover has the strongest influence on organic carbon, followed by rock formation and stream direction (moderate impact), and slope (weakest impact). Other parameters show negligible correlation with organic carbon.\u003c/p\u003e \u003cp\u003eThe relationship between organic matter and hydrogeomorphological variables mirrors that of organic carbon. This study, consistent with previous research (Singh et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Garcia, 2010; Nosrati, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wijitkosum, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alkharabshes et al., 2013; Pacheco et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Quijano et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), identifies vegetation cover as a key factor influencing sediment organic carbon, with higher vegetation cover correlating with increased organic carbon and organic matter. These studies highlight that different land uses and vegetation types vary in their capacity to enhance organic carbon, and that organic carbon retention decreases with decreasing vegetation cover.\u003c/p\u003e \u003cp\u003eSediment organic carbon decreases northward and increases southward. North-facing slopes, due to reduced sunlight exposure, exhibit higher moisture, deeper soils, greater fertility, and increased vegetation cover compared to south-facing slopes (Jendoubi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, they experience less erosion and sedimentation, minimizing organic matter and soil organic carbon loss to watersheds and streams, resulting in lower sediment organic carbon levels. Based on the findings of the RDA model, as rock formation sedimentation increases, sediment organic carbon decreases, influenced by slope aspect. Lozano- Garc\u0026iacute;a et al. (2016) emphasize the importance of considering slope direction in organic carbon estimation models. Consistent with this, Yimer et al. (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Che et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Bagherifam et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that varying slope directions significantly alter organic carbon and nitrogen stocks due to microclimatic and vegetation differences.\u003c/p\u003e \u003cp\u003eStream slope significantly influences sediment organic carbon (SOC): increased slope correlates with increased SOC. While lower elevations can exhibit higher soil organic carbon, vegetation cover mediates the relationship between slope and SOC (Mohseni and Salar, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that higher slopes generally have greater vegetation cover density, positively impacting SOC. As Battany and Grismer (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) noted, vegetation cover and soil surface condition modify the impact of slope on runoff and sediment (Zhang et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sparse vegetation on steep slopes increases runoff and sediment yield. However, adequate vegetation density mitigates the increase in sediment production with increasing slope.\u003c/p\u003e \u003cp\u003eBesides plant cover, slope orientation affects erosion and sedimentation (Sadeghi et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While Jaksic et al. (2021) found slope alone doesn't determine organic carbon content, it's influenced by vegetation, land use, orientation, soil type, land management, altitude, and other factors. Smitha et al. (2002) and Tsui et al. (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) observed higher organic carbon levels on ascending slopes, attributing this to increased vegetation accumulation, lower temperatures, and slower decomposition. Conversely, steeper slopes increase erosion and sediment transport, potentially enriching sediment with organic carbon.\u003c/p\u003e \u003cp\u003e- \u003cb\u003eAcidity (pH)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOver 90% of samples were alkaline (mean pH 7.84). pH was positively correlated with rock formation and aspect, negatively with vegetation cover and slope. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This correlation is stronger for vegetation cover and rock formation than for stream direction and slope. Other factors showed negligible or weak correlations, potentially due to near-vertical vectors.\u003c/p\u003e \u003cp\u003eIncreased erodibility correlates with higher, more alkaline sediment pH, confirming a positive relationship between pH and erosion intensity. This aligns with findings by Salmasi and Ahmadi (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) who demonstrated that increased pH in marly terrains indicates greater erosion and can be used to classify erosion types. They suggested adjusting soil pH as an erosion control strategy. Matsumoto et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) attributed higher erosion rates at elevated pH levels to increased repulsive forces between soil particles.\u003c/p\u003e \u003cp\u003eResearch indicates that pH increases on north-facing slopes and decreases on south-facing slopes. However, given the weak negative correlation between pH and altitude, the relationship between pH and slope direction may vary across altitude classes, consistent with findings by Jolokhava et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Begum et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, Tamene et al. (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found no clear relationship between acidity and slope direction.\u003c/p\u003e \u003cp\u003eRDA modeling and previous studies (Moradi and Ahmadi-Pour, 2006; Heidarian Aghakhani et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) show a negative correlation between vegetation cover density and sediment pH. Reduced vegetation cover and organic matter lead to increased alkalinity and basic cations, which promote soil aggregate dispersion and erosion, consequently raising sediment pH. Conversely, increased vegetation cover and organic matter result in lower, more acidic pH levels.\u003c/p\u003e \u003cp\u003eBoussaadi and Mouzai (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated a strong correlation between pH and species diversity in vegetation cover. Zaimes et al. (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed that pH decreased with increasing vegetation cover density. RDA modeling further indicated that vegetation cover and formation erodibility significantly correlated with and explained pH levels. Higher pH increases alkaline cations like Na+, which can degrade soil particles, leading to erosion and acidic sediments. Conversely, Saeidian and Moradi (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that in acidic soils (pH 4\u0026ndash;7), increased aluminum ion activity contributes to soil particle accumulation. As pH increases, saturated aluminum decreases while basic cations with high swelling capacity increase.\u003c/p\u003e \u003cp\u003epH exhibits a negative correlation with stream slope percentage, consistent with Zare Mehrjerdi et al. (\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Karamian and Hosseini (2016). Because increased slope and pH accelerate erosion, especially in erosion-prone areas with alkaline tendencies, the influence of vegetation density and type, lithology, slope aspect and direction, altitude, and other environmental factors should be considered holistically. This integrated approach applies not only to slope but also to other parameters as revealed by the model.\u003c/p\u003e \u003cp\u003eSlope percentage, like slope direction, influences acidity (pH), as determined by considering vegetation cover and geological unit erodibility. The consistently alkaline sediments (mean pH 7.84, CV 4.22) throughout the study area, coupled with the semi-arid climate, formations, and medium to poor rangelands with high grazing intensity, indicate high erodibility and sediment production potential in a significant portion of the sampling areas and sub-basins.\u003c/p\u003e \u003cp\u003e-\u003cb\u003eElectrical conductivity (EC)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe RDA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicates that EC Positively correlated with temperature and rock formation, negatively with precipitation and elevation. EC was generally low (\u0026lt;\u0026thinsp;4 dS/m), indicating non-saline sediments. Temperature and rock formation have a strong positive correlation with EC, while precipitation and altitude exhibit a strong negative correlation. Thus, EC increases with higher temperature and rock erodibility, and decreases with higher precipitation and altitude. EC shows little to no correlation with stream slope and vegetation cover. Consistent with Feyznia and Jafari (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), this study found high EC in easily erodible evaporite marl sediments and low EC in less erodible igneous rocks.\u003c/p\u003e \u003cp\u003eAsrari et al. (2012) found that electrical conductivity significantly influences plant community formation, which is often linked to temperature-driven evaporation concentrating soil salts and leading to high EC sediments. Elevated EC, particularly from sodium and ammonium, can exacerbate erosion due to soil swelling. Conversely, Smith et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) projected that increasing temperatures and decreasing precipitation would lower EC in steppe-shrub ecosystems.\u003c/p\u003e \u003cp\u003eIn arid and semi-arid regions, salt accumulation near the soil surface results in sediments with high electrical conductivity (EC) after erosion, potentially indicating increased erodibility. This aligns with research by Saeed et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Kanagaraj et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which found a negative correlation between altitude and EC. Higher cation concentrations increase EC. Sediments in this study, predominantly below 4 ds/m, were classified as non-saline, with higher EC values observed in areas lacking vegetation and subjected to heavy grazing.\u003c/p\u003e \u003cp\u003e- \u003cb\u003ePotassium (K)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePotassium (K) is vital for plant growth, yield, quality, and stress resistance (Lu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Romheld and Kirkby, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zorb et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), influencing soil erosion and sediment generation.\u003c/p\u003e \u003cp\u003ePotassium Positively correlated with temperature, vegetation cover, and slope, negatively with elevation and precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, temperature, vegetation cover, rock formation, and slope show a positive correlation, while elevation, precipitation, and stream slope direction show a negative correlation. Temperature, vegetation cover, slope, direction, and rock formation are the strongest predictors of sediment potassium variation.\u003c/p\u003e \u003cp\u003eConsistent with Keramati jobedar et al. (2013), this research also found highly variable mineral concentrations in areas with high temperatures and rainfall due to pronounced soil leaching, reducing calcium, magnesium, and potassium in plants. Similar to Poormirkamali and Mahmoodabadi (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we observed that increased precipitation intensity and wind lead to organic carbon loss and reduced potassium levels. Their finding of a positive correlation between vegetation cover and potassium levels, which aligns with our results, suggests that reduced vegetation density may contribute to lower potassium levels in soils and sediments.\u003c/p\u003e \u003cp\u003eAnnual potassium loss through leaching and erosion exceeds that of other elements, substantially reducing plant-available potassium. Increasing vegetation cover can mitigate erosion and sedimentation, addressing this complex issue. Varmaghany et al. (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that altitude, precipitation, and temperature influence soil and plant mineral composition. Climate factors are the most significant variables affecting potassium levels (Li et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lybrand and Rasmussen, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mavris et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oborn et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Altitude, influencing microclimate, can also affect soil formation and potassium concentration (Blanchet et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Charan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeavy rainfall leaches potassium (Francos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with leaching rates increasing in sediments, possibly due to elevation and vegetation cover. This study found that sediment potassium content rises with slope percentage, likely from erosion and subsequent enrichment, consistent with Rahimi Ashjerdi and Ayoubi (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who attributed this to clay movement and associated potassium particles on steep slopes. AL-Qahtani (1998) also found a positive correlation between slope length and potassium levels.\u003c/p\u003e \u003cp\u003eNorth-facing slopes tend to have lower sediment potassium content, potentially due to improved moisture and vegetation, which reduce erosion compared to southern slopes. Hua et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) linked potassium levels to both slope direction and percentage. While vegetation correlates directly with potassium, other influencing parameters must also be considered.\u003c/p\u003e \u003cp\u003e-\u003cb\u003eLime\u003c/b\u003e\u003c/p\u003e \u003cp\u003elime, positively correlated with slope, vegetation cover, and temperature, negatively with elevation and precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Kamali et al. (2011) found a significant relationship between lime percentage and formation erodibility, which varies across units due to other factors. Studies by Zare-Chahoki and Shafi-Zadeh, 2008 further indicate a strong link between vegetation cover and lime percentage. Lime content in soil and sediments varies with vegetation type. Zare Chahoki et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) identified it as a key factor differentiating vegetation types. Given lime's ability to neutralize acidic soils and the alkaline pH of sediments in the study areas, a significant relationship likely exists between vegetation cover and lime percentage in the region, as reflected in the sediments.\u003c/p\u003e \u003cp\u003ePrevious research supports this study's findings on the link between lime content and sedimentation. Castro and Logan (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) demonstrated that lime, by increasing pH, promotes hydroxyl group ionization on clay surfaces, creating negative charges. Simultaneously, calcium cations facilitate clay aggregation and stability, reducing erosion. Vaezi et al. (2020) further emphasized a significant correlation between lime percentage and reduced soil erodibility, indicating that lime enhances aggregate stability and permeability, thereby decreasing erosion intensity and sediment yield. Similarly, Shourijeh et al. (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that hydrated lime, rich in calcium, decreases erodibility by increasing critical shear stress and reducing lime in sediments. These results align with Costa et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who observed reduced erodibility in lime-amended soils, and Chen et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported enhanced clay compaction and decreased erodibility with increased calcium carbonate levels.\u003c/p\u003e \u003cp\u003eIn the sampled areas, increased precipitation correlates with a lower proportion of lime in sediments, likely due to increased vegetation cover, a more acidic environment, and reduced availability of alkaline cations like calcium. This reduction in lime, primarily calcium carbonate, can decrease lime enhancement of sediments. Given the low coefficient of variation and limited spatial variability of low precipitation in the study area, this relationship is likely influenced by interactions with other environmental and physicochemical variables (Mirhosseini et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, lime positively correlates with coarse sand, organic matter, and soil permeability, reducing runoff (Vaezi and Haghani, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This suggests lime plays a key role in regulating runoff in semi-arid soils, potentially reducing its presence in erosion sediments. Elevation is inversely correlated with lime and slope direction, consistent with Imran et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)'s findings. However, the relationship between stream height and lime percentage differs between northern and southern slopes. Northern slopes, with higher moisture, vegetation cover, acidity, and reduced alkaline cations, exhibit lower lime levels than southern slopes. Finally, higher slope percentages correlate with a slight increase in lime in sediments, possibly due to increased erosion and reduced vegetation, supporting Zare-Khormizi et al. (2012)'s results.\u003c/p\u003e \u003cp\u003e- \u003cb\u003eBulk Density\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBulk density Positively correlated with precipitation, elevation, and vegetation cover, negatively with temperature and rock formation. Prior studies (Bahrami et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) emphasize vegetation's influence on bulk density, porosity, and soil permeability. Crucially, plant species and root system characteristics should also be considered, as they affect pore density and porosity, thereby impacting bulk density. Mora and L\u0026aacute;zaro (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) corroborate this, noting bulk density variations among plant species. The study areas, characterized by diverse vegetation and moderate to heavy grazing, likely experienced soil compaction and increased bulk density.\u003c/p\u003e \u003cp\u003eThe RDA model reveals that increasing altitude correlates with decreased air temperature, negatively impacting microbial activity. Simultaneously, increased precipitation intensifies runoff and erosion, leading to greater soil and sediment compaction, reduced porosity and permeability, and thus, increased bulk density. Mohr et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Kavian et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) corroborate the positive relationship between bulk density and runoff.\u003c/p\u003e \u003cp\u003eGiven the average sediment bulk density of 1.3 g/cm\u0026sup3; at sampled locations, conditions are intermediate. Finer-textured areas exhibit lower specific gravity due to increased soil aggregates and pores, also reducing specific gravity through erosion. Therefore, assessing rock formation erodibility relative to sediment bulk density requires consideration of soil texture, structure, land use, other physicochemical characteristics, and organic matter.\u003c/p\u003e \u003cp\u003e- \u003cb\u003eTexture\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe analyzed sediment samples were primarily sandy, sandy-loamy, or loamy-sandy (light sediments), indicating similar light textures in adjacent slope soils (over 80% sand, under 20% silt and clay). This loose, fragile soil structure exhibits poor water retention and nutrient deficiencies, likely contributing to increased erosion and high sediment production. Compounding this issue, moderate to heavy livestock grazing and the inherent erodibility of the area's geological formations (sandstone, marl, conglomerate, and alluvial deposits, comprising about 60% of the area) necessitate soil enhancement and conservation measures.\u003c/p\u003e \u003cp\u003e-sand\u003c/p\u003e \u003cp\u003eThis study reveals that sand content is positively correlated with altitude, rainfall, and slope direction, but negatively correlated with slope and temperature. Correlations with rock formation and vegetation cover are weaker due to other influencing factors. Consistent with Charan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a significant positive correlation between sand percentage and altitude was observed, although Saeed et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found a negative correlation with altitude fluctuations. Increased sand content leads to coarser soil texture, reduced particle cohesion, and increased sand percentage through leaching. Smaller particles promote moisture retention, benefiting vegetation cover, though plant species also play a role. Chen et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Parsamehr et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) highlight the effectiveness of ground cover, like straw mulching, in managing and reducing coarse sediments such as sand.\u003c/p\u003e \u003cp\u003eKordian Hamedani et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that slope length and direction, rather than slope alone, influenced sediment production rates. Conversely, Vaezi and Ebadi (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed that sand content decreased with increasing slope, while silt and clay remained unaffected. Therefore, considering both slope percentage and direction is beneficial when analyzing sediment particle size distribution. Increased soil temperature reduces moisture, potentially decreasing vegetation cover, loosening soil particle adhesion, and leading to a higher proportion of finer particles (clay) due to erosion and slope. Hydrogeomorphological parameters impact particle size distribution, with soil aggregate arrangement and particle structure also significantly influencing pore space and the relative amounts of sand, silt, and clay.\u003c/p\u003e \u003cp\u003e-Silt and Clay\u003c/p\u003e \u003cp\u003eRedundancy analysis (RDA) indicated that silt content is directly associated with slope, temperature, and vegetation cover, but inversely associated with slope direction, precipitation, and elevation. Silt's association with rock formation was weak. Clay content showed a positive correlation with temperature and rock formation, and a negative correlation with precipitation and elevation, with minimal association to slope direction, vegetation cover, and slope. The increased clay content observed with rising temperature and rock formation erodibility suggests that higher temperatures, promoting evaporation and reducing moisture, lead to looser soil aggregates and finer clay particles. This effect likely extends to silt as well.\u003c/p\u003e \u003cp\u003eThe RDA model further revealed that increased sedimentation is linked to higher clay content. Areas dominated by clay are generally less erodible (Rezaei, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), though the specific clay mineral composition is critical. Clays with high silica to iron/aluminum oxide ratios are prone to expansion upon wetting, resulting in unstable aggregates, whereas clays with lower ratios exhibit greater water resistance and reduced erodibility.\u003c/p\u003e \u003cp\u003eOur findings align with Badia et al. (2016), who observed an inverse relationship between elevation and silt-sized particles. Furthermore, consistent with Vaezi et al. (2017), who documented a significant correlation between rainfall and sand, silt, and clay, this study also found a meaningful negative correlation between rainfall and both silt and clay, highlighting the importance of considering rainfall's impact on reducing silt and clay content in sediments in conjunction with vegetation cover and slope direction.\u003c/p\u003e \u003cp\u003eDue to higher precipitation and vegetation on northern slopes, humus colloids bind clay particles, enhancing soil cohesion and reducing erosion, thus minimizing soil fragility and the formation of rills and slices. This is also beneficial for species cover. Soofi and Emami (2007) found that southern slopes exhibit greater erosion than northern slopes. Matus (2021) links organic matter content to the cycle and clay accumulation in sediments.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStepwise Regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;(5) presents the results of the stepwise regression modeling process, which was conducted in three stages to derive the final optimal model, improving the relationship between variations in the dependent variable and the independent or explanatory variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStepwise Regression Models For IQI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntered Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcluded Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStepwise (Criterion: F probability for entry\u0026thinsp;\u0026le;\u0026thinsp;0.05 and for removal\u0026thinsp;\u0026ge;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStepwise (Criterion: F probability for entry\u0026thinsp;\u0026le;\u0026thinsp;0.05 and for removal\u0026thinsp;\u0026ge;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStepwise (Criterion: F probability for entry\u0026thinsp;\u0026le;\u0026thinsp;0.05 and for removal\u0026thinsp;\u0026ge;\u0026thinsp;0.1)\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 the IQI modeling process, at each stage, the variables demonstrating the strongest correlation with the dependent variable were retained. In the final step, three independent environmental variables -elevation (meters), vegetation density (percentage), and slope (percentage)- showed a stronger association with sediment quality compared to other factors and were more effective in explaining its variability or variance.\u003c/p\u003e \u003cp\u003eElevation showed a significant negative correlation (β = -0.393, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while vegetation cover and slope had positive correlations (β\u0026thinsp;=\u0026thinsp;0.235 and 0.225, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Low collinearity (tolerance\u0026thinsp;=\u0026thinsp;0.99, VIF\u0026thinsp;\u0026asymp;\u0026thinsp;1) confirmed model robustness (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(6) presents the details and coefficients related to the stepwise regression modeling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients and Results of Stepwise Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003estandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCollinearity statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.2363E\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.630E\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.949E\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.008\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 elevation parameter exhibits a significant inverse correlation with the IQI, as evidenced by unstandardized (B = -0.000694) and standardized (β = -0.393) coefficients at a significance level of \u0026lt;\u0026thinsp;0.01. In contrast, vegetation cover and slope parameters demonstrate a positive correlation with IQI, with unstandardized coefficients (B) of +\u0026thinsp;0.001 and +\u0026thinsp;0.002, and standardized coefficients (β) of +\u0026thinsp;0.235 and +\u0026thinsp;0.225, respectively, at a significance level of \u0026lt;\u0026thinsp;0.05. Among these, the elevation parameter, with a β value of -0.393, has the most substantial influence on sediment quality, while the slope parameter, with a lower positive β value, shows a comparatively weaker correlation and impact relative to elevation and vegetation cover.\u003c/p\u003e \u003cp\u003eThe collinearity among the independent variables in this model is minimal, as indicated by tolerance coefficients of 0.99, demonstrating nearly no linear interdependence. This is further supported by the variance inflation factor (VIF), which confirms an extremely low level of collinearity among the hydrogeomorphological variables. The standard error of the final model underscores its effectiveness in predicting changes in the dependent variable (sediment quality) using the independent variables, as well as the robustness and suitability of the proposed model (Eq.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e(Eq.\u0026nbsp;2) IQI\u0026thinsp;=\u0026thinsp;0.421\u0026ndash;0.0000694 Elevation (m)\u0026thinsp;+\u0026thinsp;0.001 Vegetation Cover (%)\u0026thinsp;+\u0026thinsp;0.002 Slope (%)\u003c/p\u003e \u003cp\u003eBased on the results of the stepwise regression model and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, lower elevations are more effective in enhancing sediment quality, as indicated by the inverse relationship between elevation and IQI; an increase in elevation corresponds to a decline in sediment quality at the sampled stream locations. Conversely, higher vegetation cover density and greater slope percentages are directly correlated with an increase in the sediment quality index.\u003c/p\u003e \u003cp\u003eThe RDA model analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveals that vegetation cover is directly correlated with slope, while slope negatively correlates with altitude. Additionally, there is a weak inverse relationship between vegetation cover and altitude. Altitude may also influence soil formation by affecting the microclimate. Soil formation is slower at higher altitudes, such as mountain regions, due to lower temperatures and reduced microorganism activity. This results in sparser vegetation and more rocky formations like pebbles, boulders and rock mass. Evidence from the sampling locations shows that most vegetation is found at lower elevations, where the conditions of temperature and humidity are more favorable, along with the transport of fine sediments and micronutrients through weathering, erosion, and downstream accumulation.\u003c/p\u003e \u003cp\u003eVegetation cover density increased on both the southern highlands and northern lowlands, particularly on slopes of 10 to 30 percent. However, as slopes exceeded 30 percent, density decreased. This increase in vegetation cover was linked to higher levels of organic matter, organic carbon, and potassium, leading to sediments rich in organic content and an improved sediment quality index. The presence of humic colloids alongside clay colloids suggests the likelihood of finer-grained sediments.\u003c/p\u003e \u003cp\u003eIQI comprises 10 physicochemical sediment characteristics, illustrating the relationship between these properties and hydrogeomorphological parameters. A higher IQI signifies better sediment quality, characterized by increased organic carbon and potassium content, finer grains, lower acidity and electrical conductivity, and optimal lime and bulk density levels. The integrated sediment quality index assesses these attributes collectively and interdependently. As a result, the sediment quality index can be utilized as a benchmark for environmental assessments, such as land degradation, in watersheds.\u003c/p\u003e \u003cp\u003eIQI values ranged from 0.22 to 0.68, with a coefficient of variation of 21% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher IQI values were associated with lower elevations, higher vegetation cover, and moderate slopes (10\u0026ndash;30%).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the changes in the integrated sediment quality index (IQI) at sampling locations, revealing variations and sinusoidal patterns in sediment quality throughout the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom sampling points 40 to 70, variations were significant, with the highest IQI of 0.68 at site 40 (south of the basin) and the lowest at site 50 (north of the basin). Analysis of organic matter reveals higher quantities at site 40, which also has a finer particle size distribution, at 79 percent, compared to 22 percent at site 50. Moreover, the average vegetation cover on the slopes aligns closely with changes in the sediment quality index. The coefficient of variation (CV) for the IQI is about 21 percent, indicating uniform sediment quality with minimal change across the sampled points in the watershed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eResearch on sediment quality has primarily focused on small watersheds, largely analyzing suspended sediments qualitatively. In contrast, this study provides a comprehensive assessment of sediment quality in Ardabil province, integrating 10 physicochemical properties into the IQI. Unlike previous studies focusing on suspended sediments or small watersheds (Amezcua-Allieri and Gonz\u0026aacute;lez-Mac\u0026iacute;as, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), this research examines streambed sediments across a diverse region. The IQI effectively captures sediment quality variations, with higher values indicating finer textures, higher organic content, and lower EC and pH, consistent with findings in other regions (Kim et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Abou El-Anwar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt also analyzed and statistically assessed the relationship between these parameters and the environmental factors of the watershed. Accordingly, sediment quality was assessed using a quality index based on physicochemical characteristics, and its relationship with environmental factors was evaluated. Research on sediment quality at the watershed scale (streams and slopes) has been limited thus far. The research area must encompass various environmental and hydrogeomorphological elements to examine their influence on sediment attributes. Ardabil province in northwestern Iran was selected as the study basin due to its diverse environmental conditions, ongoing erosion and sedimentation issues, and significant annual financial resources dedicated to managing these challenges and implementing water and soil conservation efforts.\u003c/p\u003e \u003cp\u003eAmezcua Allieri and Gonz\u0026aacute;lez-Mac\u0026iacute;as (2002) assessed river sediment quality in an industrial region using the sediment quality index (SQI), which incorporates parameters such as organic matter, pH, nitrogen, phosphorus, cation exchange capacity, nickel, and vanadium. They found that the SQI is an effective tool for evaluating the area's environmental condition.\u003c/p\u003e \u003cp\u003eSediment quality in the Danube River Basin is significantly influenced by hydrogeomorphological factors like river width and depth, which affect the accumulation of hazardous substances (Šorša et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZhang et al. (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used a partial least squares regression model to examine how watershed geomorphic parameters\u0026mdash;such as topographical features, shape, roughness ratio, and drainage network\u0026mdash;affect sediment in China's Loess Plateau. The study found that watershed shape and roughness significantly influence sediment values. Kim et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assessed river sediment health quality using nine indicators and found that geochemical methods enhance the understanding of how different elements affect sediment quality. Li et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined how changes in watershed characteristics affect the quantity and quality of sediment dissolved organic matter in Jiaozhou Bay, China. The study focused on land use, landscape patterns, and watershed attributes across three sub-watersheds. Findings indicated that the qualitative aspects of sediment dissolved organic matter were more influenced by the quantitative features of the sub-watersheds than by their quantity. This work provides new insights into sediment organic matter quality and quantity. Abou El-Anwar et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) assessed river sediment quality using the SQI index and emphasized the need for regular monitoring. Crane et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) developed an index for evaluating lake sediment quality in the USA, considering total organic carbon, particle size, metabolites, and organochlorines, and encouraged further field studies by other researchers.\u003c/p\u003e \u003cp\u003eThe negative correlation between elevation and IQI reflects reduced soil formation and vegetation at higher altitudes, leading to coarser, less nutrient-rich sediments. Vegetation cover and slope enhance sediment quality by increasing organic matter and stabilizing soil aggregates, aligning with studies by Vaezi et al. (2020) and Costa et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The predominance of sandy textures and alkaline pH underscores the region\u0026rsquo;s high erodibility, exacerbated by grazing and erodible lithology (e.g., marl, sandstone).\u003c/p\u003e \u003cp\u003eLimitations include the uneven sampling distribution due to logistical constraints and the focus on streambed sediments, which may not fully represent slope dynamics. Future research should include additional sub-basins and incorporate temporal variations in sediment quality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEnvironmental factors significantly influence erosion, leading to the generation and accumulation of sediment. While numerous studies have focused on sediment volume, this research specifically examines sediment quality and its relationship with hydrogeomorphological elements. The findings indicate that the variability in hydrogeomorphological parameters and the physicochemical characteristics of sediments and soils in the study area are generally acceptable. This data diversity enhances the reliability of the statistical analysis. A key achievement of the study is the development of a sediment quality index for the watershed, which evaluates locations based on a combination of physicochemical attributes related to erosion and sediment. Areas with higher sediment quality index values indicate better sediment quality and are prioritized for erosion and sediment control, especially where sediment volume and erosion rates are also elevated.\u003c/p\u003e \u003cp\u003eThis study highlights the significant influence of hydrogeomorphological factors on sediment quality in Ardabil province. The IQI, integrating 10 physicochemical properties, is a robust tool for assessing sediment quality and prioritizing erosion control measures. Lower elevations, higher vegetation cover, and moderate slopes are associated with better sediment quality, guiding targeted conservation strategies. Enhancing vegetation cover with adapted plant species can improve soil organic content and reduce erosion. The IQI serves as a valuable metric for environmental monitoring and land degradation assessment, with applications for watershed management and soil enhancement in degraded areas.\u003c/p\u003e \u003cp\u003eIdentifying the factors affecting sediment quality enables the development of targeted plans to improve conditions in specific regions or basins. One key action is to enhance vegetation cover with compatible plant species that can significantly increase organic material and humus in the soil, while considering the site's topography, such as slope, orientation, and altitude. The sediment quality index indicates areas with higher values represent better sediment quality and should be prioritized in planning. Additionally, sediments from these areas can be used to enhance low-quality and unproductive lands and pastures.\u003c/p\u003e \u003cp\u003eThe sediment quality index is a crucial indicator for evaluating environmental conditions, land degradation, and prioritizing areas for intervention. Therefore, identifying the environmental factors that affect it in each watershed is vital. This research showed that most study locations displayed similar variability patterns in sediment quality. Future studies should examine more key areas within the province and its sub-basins.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKeyvan Khorrami and Habib Nazarnejad wrote the original text of the article. Sediment sampling was carried out by these two individuals.Ahmad Mahmoodzadeh contributed to the selection of sediment sampling sites and geographic data.Esmaeil Sheidai-Karkaj and Farrokh Asadzadeh contributed to the analysis of samples in the laboratory and statistical analysis. Artemi Sarda contributed to the statistical analysis of samples and final editing of the text.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbou El-Anwar, E., Salman, S., Asmoay, A., Elnazer, A. (2021). Geochemical, mineralogical and pollution assessment of River Nile sediments at Assiut Governorate, Egypt. \u003cem\u003eJournal of African Earth Sciences\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e, 104227. \u003cem\u003ehttps://doi.org/\u003c/em\u003e\u003cem\u003e10.1016/j.jafrearsci.2021.104227\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAdesuyi, A.A., Ngwoke, M.O., Akinola, M.O., Njoku, K.L., Jolaoso, A.O. (2016). 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Potassium in agriculture \u0026ndash; status and perspectives. \u003cem\u003ePlant and Physiology\u003c/em\u003e, 171(9): 656 \u0026ndash; 669. \u003cem\u003ehttps://doi.org/10.1016/j.jplph.2013.08.008\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sediment quality index, stepwise regression, environmental factors, soil physicochemical properties","lastPublishedDoi":"10.21203/rs.3.rs-6561262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6561262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the factors that influence sediment is vital for comprehending erosion and watershed conditions. Exploring the relationships and interactions between environmental and hydrogeomorphological factors and erosion and sediment processes is essential. In Ardabil province, northwest Iran, 98 sediment samples were collected from streambeds to investigate the relationship between hydrogeomorphological factors of watersheds and sediment quality. The integrated quality index (IQI) of sediments was calculated from their physicochemical characteristics, and its correlation with environmental factors was assessed using stepwise regression. The findings revealed an inverse relationship between Elevation and sediment quality index, with non-standard \u0026szlig; and standard \u0026szlig; coefficients of -0.000694 and \u0026minus;\u0026thinsp;0.393, respectively, significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level. Vegetation cover and slope variables show a direct correlation with IQI at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level, with standard coefficients of +\u0026thinsp;0.001 and +\u0026thinsp;0.002, and unstandardized \u0026szlig; coefficients of +\u0026thinsp;0.235 and +\u0026thinsp;0.225. The IQI indicates that a value closer to one at sampling locations signifies better sediment quality, making these sites relevant for erosion and sediment management. The sediment quality index is a crucial measure for evaluating environmental conditions and land degradation, helping to prioritize areas for intervention. Therefore, identifying the environmental factors affecting sediment in each watershed is essential.\u003c/p\u003e","manuscriptTitle":"Sediment quality evaluation utilizing hydrogeomorphological factors in northwestern Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-06 09:35:15","doi":"10.21203/rs.3.rs-6561262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-18T08:16:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-05T16:17:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T12:00:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165960742836307275480472702364558207806","date":"2025-06-06T04:56:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251988142663521960195426500626230026037","date":"2025-06-05T15:25:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-05T07:22:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245437888804165426900485377842110499765","date":"2025-06-04T09:39:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28079056209641841507128051275093033715","date":"2025-06-03T20:31:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T14:51:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-05T06:40:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T06:36:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2025-04-30T05:18:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1f6650e2-f990-4055-ae9c-ff4fedec7d8d","owner":[],"postedDate":"June 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:06:38+00:00","versionOfRecord":{"articleIdentity":"rs-6561262","link":"https://doi.org/10.1007/s12665-025-12551-3","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2025-09-23 15:57:28","publishedOnDateReadable":"September 23rd, 2025"},"versionCreatedAt":"2025-06-06 09:35:15","video":"","vorDoi":"10.1007/s12665-025-12551-3","vorDoiUrl":"https://doi.org/10.1007/s12665-025-12551-3","workflowStages":[]},"version":"v1","identity":"rs-6561262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6561262","identity":"rs-6561262","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}