The sources and influencing factors of dissolved organic carbon under high-sediment environments – A case from Wuding River Basin

River sediments are important carbon reservoirs in terrestrial–aquatic systems, and a thorough understanding of the factors that influence the sources and distribution of organic carbon in river sediments can help us understand the carbon cycling process in river ecosystems. In this study, 21 river sediment samples were collected along the upstream–downstream gradient of the Wuding River Basin. The study revealed that (1) the sources of DOC in the Wuding River Basin were spatially heterogeneous, with plankton being the largest source of DOC upstream (39%), whereas terrestrial sources were the largest source of DOC downstream (54%). (2) Influenced by geomorphic conditions and land use, the gradually increasing hydrodynamic conditions from upstream to downstream prompted the migration of surface soil organic matter and nutrients such as effective nitrogen (AN), total phosphorus (TP), and total nitrogen (TN) to the river channel, resulting in an increase in the concentration of suspended sediment, which led to the accumulation of organic carbon in the downstream section of the river. (3) Using macrogenomic techniques, microbial - driven carbon cycling processes were identified and predicted. Downstream has greater carbon - cycling potential than upstream. Upstream DOC is positively correlated with anaerobic carbon fixation and methanogenesis (p < 0.001). Downstream DOC is negatively correlated with the rTCA cycle (p < 0.001), inhibiting autotrophic fixation as microbes use available carbon. The results of this study provide data to support ecological restoration, carbon sink enhancement, and water quality assurance in high-sediment rivers.

1 Introduction

Rivers are important conduits for carbon transport in terrestrial and aquatic ecosystems. The annual dissolved organic carbon (DOC) flux of terrestrial organic carbon to the ocean through major rivers worldwide has been estimated to be 0.45–0.78 Pg (Drake et al., 2018; Ran et al., 2018). Whereas DOC in river sediments is a central carrier of this process, DOC may be buried in sediments for long periods (to form carbon sinks) (Liu et al., 2021; Smoak et al., 2013) or released by microbial decomposition into CO₂ and CH₄ (to become carbon sources) (Li Y. et al., 2025; McTigue et al., 2021). Therefore, the study of the sources of organic carbon in river sediments (vegetation apomixis, soil erosion, anthropogenic inputs, etc.) has become a focal point in the field of the global carbon cycle.

The distribution of organic carbon in river sediments is affected by a variety of environmental variables, such as pH, total nitrogen (TN) and total phosphorus (TP). Among them, pH plays an important role in the sediment organic carbon content (Zhu et al., 2024), which affects organic carbon-related microbial activity and cation exchange (Li et al., 2019), which in turn destabilizes mineral-bound organic carbon (Guo ZhiHua et al., 2014). Zhao Bin et al. studied the sediment transport system of the Yangtze and Yellow Rivers and reported that young terrestrial organic carbon (mainly from C3 plants) in the Yangtze River sediments accelerated degradation due to pH fluctuations during transport (Zhao et al., 2023). Li et al. also reported that the decomposition rate of sedimentary organic carbon was regulated mainly by pH when coastal wetlands in the Bohai Sea were studied (Li X. et al., 2025). The correlations between organic carbon and TN and TP occur because the addition of nitrogen increases the potential for carbon sequestration (Zhang S. et al., 2022), whereas the addition of phosphorus accelerates the decomposition of reactive organic carbon in sediments (Kaspari et al., 2008).

The analysis of stable isotopic compositions (¹³C and ¹⁵N) and mixing models are effective methods for assessing organic carbon sources and degradation processes (Derrien et al., 2019). For example, Klink et al. (2022) used δ¹³C–δ¹⁵N dual isotopes combined with Bayesian mixing modelling to quantify the significantly greater contribution of fungal residues to mineral-bound organic carbon (MAOM) (42.7%) than plant residues (28.9%), revealing the dominant role of the fungal community in soil carbon pool stability (Klink et al., 2022). Similarly, Yan et al. (2023) resolved through δ¹³C and C/N multivariate modelling that aquaculture sources were the largest source of sediment organic matter in the upstream estuarine section of a mangrove wetland (44.29%), whereas terrestrial sources contributed the greatest amount (62.87%) in the middle estuarine section of the East River of Evianfeng near the river mouth; in the downstream direction, there was a significant increase in the source of marine plankton (Yan et al., 2023). These studies confirmed the ability of isotopic techniques to detect complex carbon sources. In addition, microbial macrogenomics can reveal carbon cycling pathways (Steen et al., 2019; Zhang L.-Z. et al., 2024). Dai et al. (2021) reported that the peak abundance of carbon degradation genes at mid-altitudes susceptible to mineralization coincided with isotope fractionation features, which could enable cross-scale correlations of carbon source-metabolism pathways (Dai et al., 2021). These cases demonstrate that the synergistic application of stable isotopes and macrogenomics can more accurately and quantitatively resolve organic carbon sources. Therefore, the integration of stable isotope tracing with microbial functional gene analysis (e.g., genes encoding enzymes involved in carbon metabolism) can overcome the limitations of either approach.

The Wuding River basin is located in the hinterland of the Loess Plateau, which is one of the areas with the most severe soil erosion in the middle reaches of the Yellow River (Yang et al., 2024; Yanling and Zhijun, 2019). In recent years, with the implementation of ecological projects such as returning farmland to forest (grass) and terrace construction, the vegetation cover in the basin has increased significantly, and soil erosion has been initially controlled. However, the synergistic effects of human activities (e.g., agricultural intensification, aquaculture, and urbanization expansion) (Wen et al., 2022; Yu K. et al., 2022) and climate change (e.g., precipitation extremes) have led to a new complexity in the source‒sink processes of sediment organic carbon. This complexity is reflected mainly in the increased heterogeneity of organic carbon sources and the unclear proportion of contributions from natural and anthropogenic inputs. Vegetation restoration may increase carbon sequestration by reducing soil erosion, but agricultural fertilizers or sewage discharge may also input exogenous organic carbon to rivers, altering the original carbon transport pathway (Hu et al., 2021). In addition, soil microbial community reconfiguration (e.g., changes in fungal/bacterial ratios) after fallowing may accelerate or inhibit the mineralization-humification process of organic carbon, altering carbon stability and transport potential (Bastida et al., 2021; Liu et al., 2021). Therefore, identifying the sources of DOC in the Wuding River and its impact on microbial-driven carbon cycling processes is critical for regulating the health of high-sediment river water quality environments.

The upstream and downstream areas of the Wuding watershed have unique geomorphic types; with the upstream area crossing the Maowusu windswept beach, the surface matrix is highly permeable, forming hydrological characteristics mainly based on groundwater recharge, and the downstream area transitioning to the hinterland of the Loess Plateau, which is loosely porous, may lead to a large amount of erosion–driven organic carbon input into the river system. The organic carbon transport flux driven by soil erosion on the Loess Plateau accounts for more than 60% of the total organic carbon in Yellow River sand transport (Ke et al., 2024; Ran et al., 2014); as the core area of erosion on the Loess Plateau and the main source of coarse sediments in the Yellow River, the process of sediment organic carbon transport constitutes a key link in carbon transport in the Yellow River Basin. Moreover, in this complex environment, a detailed study of the sources and influential factors of DOC in the river sediments of the Wuding River Basin plays an important role in understanding the process of river carbon cycling in inland arid and semiarid areas. Therefore, given the unique climate, vegetation, and human activities in the Wuding River Basin, we propose the following hypotheses: Sediment DOC sources (soil, aquatic plants, anthropogenic inputs) show seasonal variations; DOC burial and degradation efficiency are controlled by mineral adsorption and nutrient components; sediment DOC concentration and composition influences microbial activity and structure, thereby regulating carbon cycling at the sediment-water interface. This study aims to: (1) quantify DOC source contributions using δ¹³C and δ¹⁵N mixing models; (2) identify key constraints on DOC burial and degradation; (3) reveal DOC’s influence on microbially-driven sediment carbon cycling.

2 Materials and methods

2.1 Study area and sampling

The Wuding River Basin, covering an area of approximately 30,000 km², is located in the northern part of Shaanxi Province, China (108°27′39″–110°34′22″E, 37°02′31″–38°55′52″N). The main channel of this water system, the Wuding River, originates from the northern foothills of Baiyu Mountain in Dingbian County, Shaanxi Province, and extends for 491 km, eventually merging into the Yellow River near Hekou Village in Qingjian County, Yulin city, Shaanxi Province, and becoming a first-class tributary of the Yellow River as a main stream. The main tributaries in the basin are the Yuxi River, the Lu River, the Dali River, the Huaining River, the Hailiutu River, the Nalin River, and the Heimutouchuan River. The basin has an arid and semiarid monsoon climate, with an average annual temperature of 9.5°C and an average annual precipitation of 409.1 mm, of which approximately 74% occurs from June to September, and the average annual evapotranspiration is 1,200 mm (Figure 1, data sourced from http://eia-data.com/) (HE et al., 2025). The Wuding River Basin is divided into upstream and downstream watersheds based on the direction of the river’s flow. The upstream region of the watershed mainly flows through the wind-sand grassland area, while the downstream mainly flows the loess hill and gully area. In this study, on the basis of remote sensing data (https://zenodo.org/records/12779975) from August 2023, a buffer zone with a radius of 5 km was created around each sampling point via ArcGIS version 10.2, and the areas of different land use types, such as cropland, forest, and grassland, were analysed and quantified within each buffer zone. The land use in the study area is dominated by grassland, cropland, and urban land, which account for 74.7%, 23.2%, and 1.1% of the total watershed area, respectively (Figure 2).

Figure 1

Figure 1. Map of the location of the Wuding River Basin and the distribution of the sampling sites. Red dots represent upstream sediment sampling sites and blue dots represent downstream sediment sampling sites. The thin black line represents the boundary of the Wuding River Basin in Shaanxi Province, including the main stream and tributaries.

Figure 2

Figure 2. Land-use in the Wuding River basin.

In this study, 21 sampling sites (WD1–WD21) were deployed along the mainstem and tributaries of the Wuding River in August 2024; sampling sites WD1–WD11 were located in the downstream area, whereas WD12–WD21 were located in the upstream area (Figure 1). At each sampling site, two sediment subsamples were collected using a stainless steel Peterson grab sampler within 20–30 m north and south of the central location. These subsamples were combined in a sterilized self-sealing bag to form a single representative composite sediment sample per site. From each composite sample, three aliquots were subsequently prepared: one aliquot was refrigerated at −4°C for physicochemical parameter analysis, a second aliquot was frozen at −20°C for organic carbon isotope determination, and the third aliquot was frozen at −80°C for microbial sequencing analysis.

2.2 Laboratory analysis

2.2.1 Analysis of the physical and chemical properties of sediments

The suspended solids (SS) concentration was determined via the gravimetric method. The pH of the sediment was determined via a pH meter (FE28, Mettler Toledo Instruments (Shanghai) Co., Ltd.). The total organic carbon (TOC) and DOC (see Supplementary Text S1 for details) contents were determined via an automatic organic carbon analyser (vario TOC cube, Elementar, Germany). The total nitrogen (TN) and available nitrogen (AN) contents were determined via an automatic nitrogen analyser (K1160, Shandong Haineng Scientific Instrument). The total phosphorus (TP) and available phosphorus (AP, see Supplementary Text S2 for details) contents were determined via an ultraviolet‒visible spectrophotometer (UV-1800PC, Shanghai Mepda Instrument Co., Ltd.).

2.2.2 δ¹³C composition determination

The δ¹³C isotope ratios were determined via a MAT-251 isotope ratio mass spectrometer (MAT-251, Finnigan) and calculated as Equation 1:

${\mathit{\delta }}^{13}\mathit{C}=\left(\frac{{\mathit{R}}_{\mathit{sample}}}{{\mathit{R}}_{\mathit{standard}}}-1\right)\times 1000(1)$

R_sample and ${\mathit{R}}_{\mathit{standard}}$ are the isotope ratios of the sample and standard, respectively. δ¹³C values were corrected via the national standard for stable isotopes of organic carbon GBW04407 (carbon black) based on the Vienna Peedee Belemnite (PDB) standard (δ¹³C = (−22.43‰ ± 0.07‰), and the analytical precision of δ¹³C was less than 0.2‰.

2.2.3 Carbon cycle functional genes

After microbial DNA extraction, library construction, and Illumina sequencing, species annotation was performed using kraken2 and self-built database (Wekemo Tech Co.) to annotate and categorize the clean sequences in the samples, and Bracken to predict the actual relative abundance of species in the samples. The clean reads after quality control and de-hosting were aligned to the UniRef90 protein database using HUMAnN3 software (Franzosa et al., 2018). Based on the correspondence between UniRef90 IDs and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) database, the gene abundance table was generated, and then the functional abundance profiles were plotted for each sample.

2.3 Statistical modeling and data analysis

Stable carbon and nitrogen isotope three-terminal elemental Bayesian Monte Carlo (Bayesian Markov chain Monte Carlo (MCMC)) models can be used to quantify the relative contributions of potential sources to DOC. The δ¹³C‒δ¹⁵ N isotope mixing equations read as Equation 2 (Li et al., 2020; Yan et al., 2023):

$\begin{array}{l}{\left(\frac{{\hspace{0.17em}}^{13}C}{{\hspace{0.17em}}^{12}C}\right)}_{mixture}={\displaystyle \sum _{i=1}^n}{x}_i{K}_i^C{\left(\frac{{\hspace{0.17em}}^{13}C}{{\hspace{0.17em}}^{12}C}\right)}_i/{\displaystyle \sum _{i=1}^n}{x}_i{K}_i^C\\ {\left(\frac{{\hspace{0.17em}}^{15}N}{{\hspace{0.17em}}^{14}N}\right)}_{mixture}={\displaystyle \sum _{i=1}^n}{x}_i{K}_i^N{\left(\frac{{\hspace{0.17em}}^{15}N}{{\hspace{0.17em}}^{14}N}\right)}_i/{\displaystyle \sum _{i=1}^n}{x}_i{K}_i^N\\ {\displaystyle \sum _{i=1}^n}{x}_i=1\end{array}(2)$

Sources of δ¹³C and δ¹⁵N end-member values for DOC were selected based on the major land-use types within and around the Wuding River Basin and previous research in the field, including: terrestrial plant/soil sources, plankton sources, and sewage sources. The mean values of δ¹³C and δ¹⁵N measured in samples from each source were defined as the end-member values (Cai et al., 2004; Liu and Xing, 2012; Yang et al., 2023). The δ¹³C and δ¹⁵N isotope ratios of terrestrial source end elements were −25.74‰ ± 0.40‰ and 11‰ ± 1.46‰, respectively (Rao et al., 2017); whereas the δ¹³C and δ¹⁵N isotope ratios of zooplankton end elements were −23.7‰ ± 0.96‰ and 7‰ ± 0.95‰, respectively (Gu et al., 2017; Yan et al., 2023); and the δ¹⁵N isotope values of domestic sewage sources δ¹³C: −25.32‰ ± 1.00‰, δ¹⁵N: 10‰ ± 0.28‰ (Machiwa, 2010). δ¹⁵N isotope values were extracted by kriging interpolation and smoothing on the basis of data from the August 2023 contemporaneous study of Xu et al. (2023) (Xu et al., 2023).

The R language program package MixSIAR 3.6.3 was used to quantify the contributions of sources of DOC in the sediments of the Wuding River Basin. Statistical analyses, including correlation analysis, t-test, and principal component analysis (PCA), were performed via Origin 2024 software (Microcal Software, Inc., Northampton, MA). Graphs for this study were plotted via ArcGIS 10.5 and Origin 2024b software.

3 Results

3.1 Physical and chemical properties of river sediments in the Wuding River basin

The SS and physicochemical properties of the river sediments in the Wuding River Basin exhibited spatial heterogeneity (Table 1). The overall variation in the SS content in the river was in the range of 5–247056 mg/L, with significantly higher SS concentrations downstream than upstream (Supplementary Figure S1). Sediment pH exhibited alkaline to strongly alkaline conditions (range: 8.26–8.94; mean ± SD: 8.63 ± 0.18, n = 21), with significantly higher values observed upstream (8.70 ± 0.14) than downstream (8.58 ± 0.20) (p > 0.05; Figure 3A; Supplementary Table S1). The TOC content ranged from 0.32 to 4.29 g/kg, the mean value of the upstream TOC content was 0.90 g/kg, the mean value of the downstream TOC content was 2.30 g/kg, the TOC content increased by 155.6% from upstream to downstream, and there was a significant difference between the upstream and downstream values (p < 0.05) (Figure 3B; Supplementary Table S1). The mean TP concentration was 0.36 g/kg upstream and 0.48 g/kg downstream, with coefficients of variation of 47.22% (upstream) and 14.58% (downstream), respectively, and the spatial distribution showed a trend towards downstream enrichment, where the TP concentration increased by 33.3% from upstream to downstream (Figure 3C; Supplementary Table S1). The range of AP concentrations was 1.14–10.56 mg/kg, and the mean AP concentration was 2.30 mg/kg upstream and 6.66 mg/kg downstream, with significant differences (p < 0.001) between upstream and downstream AP (Figure 3D; Supplementary Table S1). The mean TN concentration was 0.07 g/kg upstream and 0.26 g/kg downstream, and the TN concentration increased by 271.4% from upstream to downstream, with a significant difference between upstream and downstream (p < 0.001) (Figure 3E; Supplementary Table S1). The mean AN content both upstream and downstream was 0.03 g/kg, and there was no statistically significant difference between upstream and downstream (p > 0.05) (Figure 3F; Supplementary Table S1). In brief, AP, TN and TOC were significantly different upstream and downstream.

Table 1

Table 1. Physical and chemical properties of the sediments in the Wuding River.

Figure 3

Figure 3. Spatial distribution of physical and chemical properties in the sediments of the Wuding River. These factors in the subplot were pH (A), TOC (B), TP (C), AP (D), TN (E), AN (F).

3.2 DOC geochemical characterization of river sediments in the Wuding River basin

The DOC content of river sediments in the Wuding River Basin ranged from 27.78 mg/kg to 77.45 mg/kg (mean 53.06 ± 15.78 mg/kg). The mean DOC content was 39.35 mg/kg upstream and 65.53 mg/kg downstream, and the DOC content increased by 66.53% from upstream to downstream; the difference in the DOC content between upstream and downstream areas was significant (p < 0.001) (Figure 4).

Figure 4

Figure 4. Spatial distribution of the DOC content in the sediments of the Wuding River.

The C/N values, δ¹³C and δ¹⁵N compositions of river sediments in the Wuding River Basin ranged from 6.42 to 46.38, −27.62–23.06, and 1.04‰–11.40‰, respectively. The C/N values ranged from 2.12 to 29.22 (mean 9.84) upstream and from 1.60 to 4.21 (mean 2.71) downstream; the δ¹³C ratio ranged from −23.06–27.62‰ (mean 6.58‰ ± 1.22‰) upstream and δ¹³C ranged from −24.04 to −26.21‰ (mean −25.11‰ ± 0.68‰); the δ¹⁵N ratio ranged from 1.04‰ to 10.36‰ (mean 6.85‰ ± 3.07‰) upstream; and the δ¹⁵N ratio ranged from 8.80‰ to 11.40‰ (mean 9.86‰ ± 0.79‰) downstream (Table 1).

In this study, TOC and TN were significantly positively correlated (r = 0.78, p < 0.001, Spearman’s rank correlation coefficient) (Figure 5A), and the strong correlation between DOC and TN may indirectly indicate the stability of source singularity or the mixing ratio. The C/N values for the sediments in the Wuding River Basin ranged from 7.12 to 43.80 (mean 14.05), with upstream C/N values ranging from 6.42 to 46.38 (mean 20.12) and downstream C/N values ranging from 6.88 to 10.38 (mean 8.46); in this study, the mean C/N value was 13.77, indicating a mixed source. There was a significant negative correlation between δ¹³C and C/N (Figure 5B), and in general, the δ¹³C values of terrestrial higher plants (approximately −25 to −30‰) were lower than those of aquatic organisms (approximately −20% to −25%). With increasing input of organic matter from terrestrial sources, the TOC content increases, and the proportion of terrestrial materials in sediments increases, resulting in lower δ¹³C values. There was a positive correlation between δ¹⁵N and C/N (Figure 5C), and the sediment C/N value and δ¹⁵N composition gradually increased from upstream to downstream of the Wuding River sediments.

Figure 5

Figure 5. Correlations between (A) TOC and TN, (B) δ¹³C and DOC, and (C) δ¹⁵N and DOC in river sediments in the Wuding River Basin.

3.3 Quantification of sources of DOC in river sediments in the Wuding River basin

Spatial heterogeneity was observed in the allocation of DOC sources to sediments from the Wuding River Basin. A three-terminal element mixing model was constructed with typical δ¹³C and δ¹⁵N compositions to quantify the relative contributions of three potential sources of DOC (terrestrial sources, sewage sources and planktonic sources). Figure 6 shows the results of the Monte Carlo modelling of the proportional contribution of DOC sources. In the upstream region, terrestrial sources contribute 35% ± 0.011%, sewage sources 34% ± 0.052% and planktonic sources 39% ± 0.030%. In contrast, in the downstream region, the Terrestrial contribution increased significantly to 54% ± 0.034%. The sewage contribution decreased to 31% ± 0.018%, and the plankton contribution decreased to 18% ± 0.022%. Although the endogenous contribution remained high in the river system (34%–31%), the terrestrial contribution gradually increased as the river moved from upstream to downstream (Figure 6).

Figure 6

Figure 6. Potential source contributions of sediment DOC in the Wuding River Basin using a mixing model.

4 Discussion

4.1 Contributions of three DOC sources in river sediments in the Wuding River basin

In this study, it was found that the terrestrial contributions was significantly higher in the downstream (55.7%) than in the upstream (12.7%) (Figure 7; Supplementary Table S2). Although the land use within the 5 km buffer zone of the whole Wuding River basin is dominated by grassland and cultivated land, and there is no significant difference in the proportion of cultivated land coverage between the upstream and downstream (Figure 2), the downstream is more directly adjacent to the intensive agricultural activities and urban areas. This spatial proximity means that downstream receiving sites are more susceptible to surface runoff input from these areas of high human activity. This spatial proximity means that the downstream is more susceptible to surface runoff input from these areas of high human activity. The contribution of terrestrial organic matter is significantly enhanced by fertilizer, sewage, and associated fulic acid exogenous organic carbon carried by surface runoff (Lambert et al., 2017; Varma et al., 2022). Wu et al. (2023) supported this view by finding that the concentration of biodegradable organic matter (BDOM) in urbanized areas was significantly higher than that in natural areas, and that the proportion of agricultural and urban land was positively correlated with the fluorescence fraction of terrestrial sources (Wu et al., 2023). It follows that the intensity of human activity, rather than the simple proportion of LU types, through enhanced surface runoff input, is the key factor driving the significantly higher contribution of downstream terrigenic sources. In addition to anthropogenic inputs, differences in DOC sources between upstream and downstream may also be influenced by other factors, such as potential differences in the specific species composition of C3 plants in vegetation communities, soil organic matter turnover processes, and changes in river hydrodynamic conditions such as water velocity through sediments and residence time of organic matter. For example, the δ¹³C values of sediments in the Wuding River basin fall within the typical range of C3 plants (−29.5‰ to −24.4‰) in both the upper (−23.06 to −27.62‰) and lower (−24.04 to −26.21‰) reaches (Zhuang et al., 2023). These factors may work together to result in a more significant terrigenous organic matter signal in downstream sediments.

Figure 7

Figure 7. (A) Results of the principal component analysis (PCA). (B) PCA correlation matrix for measured (hydro)geochemical parameters.

Sewage sources contributed more to the upstream (48%) than to the downstream (20.8%). The mean δ¹⁵N composition of the upstream was 6.85‰ (Supplementary Table S2), and the sources were usually more complex in the downstream of the river than in the upstream, e.g., the degree of humification and aromaticity of CDOM were significantly higher in the tributaries of the Qinghai Lake Basin than in the lake itself, with the dominant role of terrestrial source input in the downstream (Yu X.-Q. et al., 2022). In addition, Zhang et al. who studied the analysis of organic matter sources in sediments from Qingpu District, Yangtze River Delta, also showed that the contribution of soil organic matter was as high as 96.8%, diluting the proportion of sewage sources (Zhang Z.-B. et al., 2022).

The contribution of organic matter from planktonic sources was greater upstream (approximately 39.3%) than downstream (approximately 23.4%) (Supplementary Table S2). The greater contribution of the plankton source (39.3%) in the upper reaches was due to two main aspects: first, the plankton source was associated with the δ¹³C value, and when the productivity of the water body was relatively high, the plankton consumed DOC rapidly, resulting in weakened fractionation and high δ¹³C, as the mean value of δ¹³C in the upper reaches of the present study was −24.52‰ higher than that in the lower reaches of the study, which was −25.11‰. Second, the geomorphological type of the upstream area is a windy sandy beach area with flat topography and slow flow, and the slow flowing waters of the river can increase the light intensity (Walks, 2007). In addition, the water quality is better, which can provide a favourable environment for the growth of plankton (Wen et al., 2020). In the downstream area, the reduced contribution of phytoplankton sources was influenced mainly by the input of organic matter from terrestrial sources. Moreover, decreased productivity and negative δ¹³C (mean −25.11‰) due to nutrient dilution or light limitation may have overlapped with C3 plant signals from terrestrial sources, which presently led to the reduced contribution of planktonic biogenic sources.

4.2 Drivers of DOC burial and degradation in river sediments in the Wuding River basin

The strong sediment transport characteristics of the Wuding River Basin lead to highly dynamic carbon exchange at the sediment‒water interface. With a high sediment background, DOC regulates its source‒sink function by altering environmental conditions and carbon burial efficiency, which in turn affects carbon sink potential of the basin. Therefore, in order to better understand the role of DOC in regulating the health of river water quality, it is necessary to consider the driving factors that influence the burial and degradation processes of DOC. In this study, PCA analysis showed that DOC was negatively correlated with pH, C/N, and δ¹³C, and positively correlated with AP, TN, and TOC (Figure 7A). The generated heatmap also showed that DOC was significantly correlated with pH, TC, AP, TP, AN, TN, C/N, δ¹³C, δ¹⁵N, and other indicators (Figure 7B).

4.2.1 SS

SS, which contains a large amount of particulate organic carbon, is an important component of carbon flux (Song et al., 2023). In this study, DOC and SS were found to be significantly positively correlated (r = 0.57, p < 0.01), and the concentration of suspended solids downstream of the watershed was significantly higher than that upstream, which was mainly because, during intense hydraulic erosion, the surface soil (rich in organic matter) and the deeper mineralized sediments were washed into the river channel at the same time, and the suspended-sediment-attached microorganisms were able to degrade the particulate organic carbon in the sediment transport process, which in turn promoted the conversion of particulate organic carbon to DOC (Gao et al., 2002; Morán and Estrada, 2002). A high suspended sediment concentration is often accompanied by a high organic carbon content and low dissolved oxygen saturation. Xia et al. (2009) reported that the smaller the particle size of the suspended sediment is, the larger the specific surface area and the higher the organic carbon content (Xia et al., 2009), which further confirms the significant positive correlation between the DOC and SS contents obtained in this study.

4.2.2 pH

pH was negatively correlated with DOC (r = −0.47, p < 0.05) (Figure 7A), which was consistent with the results of a previous study (Guo ZhiHua et al., 2014). pH affects the adsorption capacity of minerals for DOC mainly by influencing the surface charge and adsorption sites of minerals in sediments (Wang Lei et al., 2017). A lower pH results in more positively charged particles and favours the adsorption of negatively charged DOC. However, higher pH limits the binding capacity of frontal clay compounds, which in turn leads to lower levels of DOC in river sediments (Abate and Masini, 2003; Vermeer et al., 1998; Zhang A. et al., 2024). The pH range in this study was just between 8.26 and 8.94, which is slightly alkaline. In addition, pH affects microbial activity and species. In weakly alkaline to alkaline environments, certain microorganisms are more capable of decomposing organic carbon, which may accelerate the decomposition rate of DOC. This is another reason for the decrease in DOC content (Rovira and Greacen, 1957).

4.2.3 TN

As a major nutrient in sediments, nitrogen has a very important influence on carbon fixation, distribution and accumulation as well as on plant primary productivity (Batjes, 2002). We found a strong correlation (r = 0.92, p < 0.001) between the TN and TOC contents in sediments, mainly because >90% of the N in sediments is organically bound (Murphy et al., 2000), and the mechanisms of DOC and TN dynamics in sediments are usually synchronous. The effectiveness of TN directly controls the rate of DOC decomposition. The nitrogen supply can promote plant growth and thus organic carbon accumulation (Li et al., 2016).

4.2.4 C/N

C/N was significantly and positively correlated with DOC (r = −0.51, p < 0.05) (Figure 7A). The C/N value ranges from four to six and is typically less than 10. However, the C/N value at sampling points W13, W15, and W17 exceed 10, indicating that the DOC at these sampling points may influence the input of cellulose-rich terrestrial plants (C/N > 10). These findings indicate that the DOC at these sites may be dominated by cellulose-rich terrestrial plant inputs (C/N > 10). Terrestrial plants are composed of lignin and cellulose, which are nitrogen-poor (Yan et al., 2023).

4.2.5 AN and AP

AN was significantly and positively correlated with DOC (r = 0.50, p < 0.05) (Figures 7A,B), suggesting that changes in the content of effective nitrogen, as a plant growth limiting factor, affect ecosystem organic matter output, which in turn affects DOC levels (Bei et al., 2022). Adequate AN content favours the growth and reproduction of plants, which in turn increases plant biomass (LeBauer and Treseder, 2008). Increased plant biomass means that more organic matter will enter the sediments and increase DOC levels (Cotrufo et al., 2013). Moreover, microorganisms also need nitrogen sources in the process of decomposing and transforming organic matter (Sinsabaugh et al., 2016), appropriate AN levels can maintain microbial activity and promote the decomposition and transformation of organic matter, and part of the organic carbon will be retained in the sediment during the process, causing DOC levels to rise. AP was highly and significantly positively correlated with DOC (r = 0.79 p < 0.001), and an environment with a high effective phosphorus content may promote biological activities and accelerate organic matter synthesis and accumulation. When plants grow vigorously, they will fix more carbon dioxide and synthesize more organic matter through photosynthesis, and part of this organic matter will enter the sediments in the form of apoptosis and other forms, increasing the DOC content (Huang et al., 2019). Moreover, increased microbial activity also contributes to the decomposition and transformation of organic matter (Schultz and Urban, 2008), and under suitable conditions, some organic carbon is immobilized in sediments, resulting in increased DOC contents.

4.3 Impact of DOC in river sediments of the Wuding River basin on microbially driven carbon cycling

In this study, a total of 20 carbon cycle reaction pathways were identified and predicted via the DiTing tool (CX et al., 2021). Among them, the major contributor was the TCA cycle. Overall, the downstream carbon cycle potential was greater than the upstream carbon cycle potential, such as the TCA cycle, Wood–Ljungdahl pathway (WL), glycolysis (glycolysis), and organic acid metabolism pathways (Figure 8).

Figure 8

Figure 8. Functional gene pathway analysis of the upstream and downstream carbon cycles in the river sediment WL cycle: Wood–Ljung–Dahl pathway; glycolysis: glycolysis; TRA: the total relative abundance of each pathway.

In this study, DOC was found to be correlated with the Wood–Ljungdahl pathway, the methanogen pathway, the rTCA cycle, and fermentation to lactate in the carbon cycle (Supplementary Figure S2). Among them, DOC in upstream sediments was significantly and positively correlated with methanogenesis (p < 0.001) (Figure 9A). This occurred for two main reasons: first, grassland apoplastic sediments are rich in easily decomposable carbohydrates, and their rapid decomposition can release a large amount of DOC in a short period (Wang et al., 2025); second, the high porosity of sandy sediments facilitates the migration and local enrichment of DOC (Zhan et al., 2023). Second, owing to the flat topography of windy sandy beach terrain, low surface water mobility, and slow pore water exchange in the sediment, a stable anaerobic layer is easily formed, and the presence of DOC in this environment can provide available substrate and reducing power for functional genes that act as methanogens, driving the process of CO₂ reduction to CH₄ (Sun et al., 2021; Zhou et al., 2019). In this anaerobic microenvironment, the concomitant decomposition of DOC and subsequent release of CO₂ drive carbon fixation via the Wood–Ljungdahl pathway. Therefore, DOC in upstream sediments was also significantly positively correlated (p < 0.001) with Wood–Ljungdahl (known as the reduced acetyl coenzyme A pathway, which is a key pathway for carbon fixation in the carbon cycle) (Figure 9A). Downstream, DOC in the sediments was significantly (p < 0.001) negatively correlated with the rTCA cycle (Figure 9B). This may be because high DOC concentrations provide abundant substrates (McDonough et al., 2020) for heterotrophic microorganisms (which rely on readily available organic carbon) to rapidly acquire energy through catabolism (e.g., glycolysis, fermentation) (Liao et al., 2024) without relying on the carbon fixation process of the rTCA cycle.

Figure 9

Figure 9. DOC in upstream (A) and downstream (B) sediments in relation to functional carbon cycle genes.

In summary, DOC is a key factor regulating carbon cycle pathways. DOC supports anaerobic carbon fixation and methanogenesis, realizing carbon regeneration; downstream, human interference leads to excessive DOC, breaking the autotrophic-heterotrophic equilibrium and decreasing the efficiency of carbon fixation; and watershed management needs to pay attention to the coupling of DOC and microbial functionality.

4.4 Research limitations and management suggestions

(1) This study only collected spatial scale samples at a single time node and failed to explore the trend of DOC content changes at different time scales. Future studies should comprehensively consider the changing patterns of DOC content at spatial and temporal scales to more fully understand the behavioural characteristics of DOC changes in different seasons and their impacts.

(2) For the identification of DOC sources, the carbon and nitrogen isotopic signatures provided by other studies were used as the reference basis in this study. Since the carbon and nitrogen isotope values of different sources may overlap or have slight differences, this may lead to large fluctuations in the allocation ratios of organic carbon sources calculated by the MixSIAR model, resulting in uncertainty in the quantitative analysis results. Therefore, follow-up studies should focus on determining the actual carbon and nitrogen isotopic signatures of different local DOC sources to improve the assessment accuracy.

On the basis of the results of this study, the main sources of sediment DOC in the Wuding River Basin can be identified. To effectively control sediment DOC and increase the safety of river water quality, the following recommendations are proposed on the basis of the conclusions of this study: (1) Combine with soil and water conservation measures to reduce sediment inputs, and monitor the DOC dynamics in sediments to warn of water quality risks. (2) DOC input can be reduced by controlling agricultural surface pollution and restoring vegetation buffers to protect the anaerobic microenvironments in natural river sections.

5 Conclusion

In this study, we used stable carbon and nitrogen isotope techniques and metagenomic sequencing method to identify the drivers of the source and distribution of sedimentary organic matter (DOC) in the Wuding River Basin and their influence on the microbial-driven carbon cycling process. The main findings are as follows.

(1) The contents of DOC, AN, TP, TN and SS in the sediments of the Wuding River all increased from upstream to downstream.

(2) Three sources of DOC in sediments from upstream and downstream of the Wuding River Basin were quantified based on the δ¹³C and δ¹⁵N three-terminal element mixing model.

(3) The increased hydrodynamic forces from upstream to downstream promote the synergistic transport of soil nutrients and organic matter, resulting in the cumulative effect of the “transport-deposition” of suspended sediment and organic carbon downstream.

(4) Microbial carbon metabolism is longitudinally differentiated: upstream DOC content drives the Wood‒Ljung–Dahl pathway and methanogenesis for carbon regeneration, whereas downstream excess DOC content triggers an autotrophic‒heterotrophic imbalance by inhibiting the rTCA cycle.

The research results provide a key scientific basis for carbon sink regulation and the ecological management of high-sediment rivers.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author contributions

MX: Writing – original draft, Writing – review and editing, Conceptualization, Data curation, Formal Analysis, Methodology, Software, Visualization, Supervision. NX: Data curation, Investigation, Supervision, Visualization, Writing – review and editing. BH: Investigation, Project administration, Supervision, Writing – review and editing. XG: Investigation, Writing – review and editing. ZW: Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review and editing, Software. XZ: Investigation, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work is supported by the Shaanxi Province Water Conservancy Science and Technology Program (2024slkj09), Shaanxi Provincial Science and Technology Department Key R&D Program (2024SF-ZDCYL-05-08), Basic Research Funds for Central Universities in 2024 (300102294722).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1631894/full#supplementary-material

References

Abate, G., and Masini, J. C. (2003). Influence of pH and ionic strength on removal processes of a sedimentary humic acid in a suspension of vermiculite. Colloids Surfaces A Physicochem. Eng. Aspects 226 (1-3), 25–34. doi:10.1016/S0927-7757(03)00418-7

CrossRef Full Text | Google Scholar

Bastida, F., Eldridge, D. J., García, C., Kenny Png, G., Bardgett, R. D., and Delgado-Baquerizo, M. (2021). Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J. 15 (7), 2081–2091. doi:10.1038/s41396-021-00906-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Batjes, N. (2002). Carbon and nitrogen stocks in the soils of Central and Eastern Europe. Soil Use Manag. 18 (4), 324–329. doi:10.1111/j.1475-2743.2002.tb00248.x

CrossRef Full Text | Google Scholar

Bei, S., Li, X., Kuyper, T. W., Chadwick, D. R., and Zhang, J. (2022). Nitrogen availability mediates the priming effect of soil organic matter by preferentially altering the straw carbon-assimilating microbial community. Sci. Total Environ. 815, 152882. doi:10.1016/j.scitotenv.2021.152882

PubMed Abstract | CrossRef Full Text | Google Scholar

Cai, D., Li, H., Zhou, W., liu, W., and Cao, W. (2004). Stable carbon and nitrogen isotopes in the Wudinghe drainage basin. Geochimica 33 (6). doi:10.19700/j.0379-1726.2004.06.010

CrossRef Full Text | Google Scholar

Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., and Paul, E. (2013). The M icrobial E fficiency-M atrix S tabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. change Biol. 19 (4), 988–995. doi:10.1111/gcb.12113

PubMed Abstract | CrossRef Full Text | Google Scholar

Cx, X., H, L., Xy, Z., J, L., Y, Z., G, R., et al. (2021). DiTing: a pipeline to infer and compare biogeochemical pathways from metagenomic and metatranscriptomic data. Front. Microbiol. 12, 698286. doi:10.3389/fmicb.2021.698286

CrossRef Full Text | Google Scholar

Dai, Z., Zang, H., Chen, J., Fu, Y., Wang, X., Liu, H., et al. (2021). Metagenomic insights into soil microbial communities involved in carbon cycling along an elevation climosequences. Environ. Microbiol. 23 (8), 4631–4645. doi:10.1111/1462-2920.15655

PubMed Abstract | CrossRef Full Text | Google Scholar

Derrien, M., Brogi, S. R., and Gonçalves-Araujo, R. (2019). Characterization of aquatic organic matter: assessment, perspectives and research priorities. Water Res. 163, 114908. doi:10.1016/j.watres.2019.114908

PubMed Abstract | CrossRef Full Text | Google Scholar

Drake, T. W., Raymond, P. A., and Spencer, R. G. (2018). Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. 3 (3), 132–142. doi:10.1002/lol2.10055

CrossRef Full Text | Google Scholar

Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., et al. (2018). Species-level functional profiling of metagenomes and metatranscriptomes. Nat. methods 15 (11), 962–968. doi:10.1038/s41592-018-0176-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, Q., Tao, Z., Shen, C., Sun, Y., Yi, W., and Xing, C. (2002). Riverine organic carbon in the Xijiang River (South China): seasonal variation in content and flux budget. Environ. Geol. 41, 826–832. doi:10.1007/s00254-001-0460-4

CrossRef Full Text | Google Scholar

Gu, Y.-G., Ouyang, J., Ning, J.-J., and Wang, Z.-H. (2017). Distribution and sources of organic carbon, nitrogen and their isotopes in surface sediments from the largest mariculture zone of the eastern Guangdong coast, South China. Mar. Pollut. Bull. 120 (1-2), 286–291. doi:10.1016/j.marpolbul.2017.05.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo ZhiHua, G. Z., Zhang Li, Z. L., Guo YanRu, G. Y., Wen WanYu, W. W., Cao Meng, C. M., Guo JuLan, G. J., et al. (2014). Soil carbon sequestration and its relationship with soil pH in Qinglangang mangrove wetlands in Hainan Island. doi:10.11707/.1001-7488.20141002

CrossRef Full Text | Google Scholar

He, X., Chang, T., Qiao, X., Jiang, M., and Wang, Y. (2025). Effect of land use change on groundwater hydrological process in Yuxi River Basin. Hydrogeology Eng. Geol., 1–11. doi:10.16030/j.cnki.issn.1000-3665.202405036

CrossRef Full Text | Google Scholar

Hu, H., Umbreen, S., Zhang, Y., Bao, M., Huang, C., and Zhou, C. (2021). Significant association between soil dissolved organic matter and soil microbial communities following vegetation restoration in the Loess Plateau. Ecol. Eng. 169, 106305. doi:10.1016/j.ecoleng.2021.106305

CrossRef Full Text | Google Scholar

Huang, W., Cao, X., Huang, D., Liu, W., Liu, X., and Zhang, J. (2019). Phosphorus characteristics and microbial community in the sediment-water-algal system during algal growth. Environ. Sci. Pollut. Res. 26, 31414–31421. doi:10.1007/s11356-019-06284-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaspari, M., Garcia, M. N., Harms, K. E., Santana, M., Wright, S. J., and Yavitt, J. B. (2008). Multiple nutrients limit litterfall and decomposition in a tropical forest. Ecol. Lett. 11 (1), 35–43. doi:10.1111/j.1461-0248.2007.01124.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Ke, Y., Calmels, D., Bouchez, J., Massault, M., Chetelat, B., Noret, A., et al. (2024). Channel cross-section heterogeneity of particulate organic carbon transport in the Huanghe. Earth Surf. Dynam. 12 (1), 347–365. doi:10.5194/esurf-12-347-2024

CrossRef Full Text | Google Scholar

Klink, S., Keller, A. B., Wild, A. J., Baumert, V. L., Gube, M., Lehndorff, E., et al. (2022). Stable isotopes reveal that fungal residues contribute more to mineral-associated organic matter pools than plant residues. Soil Biol. Biochem. 168, 108634. doi:10.1016/j.soilbio.2022.108634

CrossRef Full Text | Google Scholar

Lambert, T., Bouillon, S., Darchambeau, F., Morana, C., Roland, F. A., Descy, J.-P., et al. (2017). Effects of human land use on the terrestrial and aquatic sources of fluvial organic matter in a temperate river basin (the Meuse River, Belgium). Biogeochemistry 136, 191–211. doi:10.1007/s10533-017-0387-9

CrossRef Full Text | Google Scholar

LeBauer, D. S., and Treseder, K. K. (2008). Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89 (2), 371–379. doi:10.1890/06-2057.1

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, C., Li, Q., Zhao, L., Ge, S., Chen, D., Dong, Q., et al. (2016). Land-use effects on organic and inorganic carbon patterns in the topsoil around Qinghai Lake basin, Qinghai-Tibetan Plateau. Catena 147, 345–355. doi:10.1016/j.catena.2016.07.040

CrossRef Full Text | Google Scholar

Li, S., Xia, X., Zhang, S., and Zhang, L. (2020). Source identification of suspended and deposited organic matter in an alpine river with elemental, stable isotopic, and molecular proxies. J. Hydrology 590, 125492. doi:10.1016/j.jhydrol.2020.125492

CrossRef Full Text | Google Scholar

Li, X., Wang, M., Tian, W., Xu, G., and Ding, Z. (2025a). Distribution and source identification of sediment organic carbon of the coastal wetlands around Bohai Sea. Wetlands 45 (5), 46. doi:10.1007/s13157-025-01928-6

CrossRef Full Text | Google Scholar

Li, Y., Liu, Z., Li, P., Zhou, X., Deng, Z., Zou, Z., et al. (2025b). Dissolved and particulate organic carbon transport among forest, river, and wetland ecosystems: a review of processes, controlling factors, challenges, and prospects. Environ. Rev. 33, 1–22. doi:10.1139/er-2024-0078

CrossRef Full Text | Google Scholar

Li, Y., Wang, G., Wang, J., Jia, Z., Zhou, Y., Wang, C., et al. (2019). Determination of influencing factors on historical concentration variations of PAHs in West Taihu Lake, China. Environ. Pollut. 249, 573–580. doi:10.1016/j.envpol.2019.03.055

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, J., Dou, Y., Wang, B., Gunina, A., Yang, Y., An, S., et al. (2024). Soil stoichiometric imbalances constrain microbial-driven C and N dynamics in grassland. Sci. Total Environ. 924, 171655. doi:10.1016/j.scitotenv.2024.171655

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, W., Ma, L., Abuduwaili, J., Issanova, G., and Saparov, G. (2021). Sediment organic carbon sequestration of Balkhash lake in Central Asia. Sustainability 13 (17), 9958. doi:10.3390/su13179958

CrossRef Full Text | Google Scholar

Liu, W., and Xing, M. (2012). Isotopic indicators of carbon and nitrogen cycles in river catchments during soil erosion in the arid Loess Plateau of China. Chem. Geol. 296-297, 66–72. doi:10.1016/j.chemgeo.2011.12.021

CrossRef Full Text | Google Scholar

Machiwa, J. F. (2010). Stable carbon and nitrogen isotopic signatures of organic matter sources in near-shore areas of Lake Victoria, East Africa. J. Gt. Lakes. Res. 36 (1), 1–8. doi:10.1016/jjglr.2009.11.005

CrossRef Full Text | Google Scholar

McDonough, L. K., O'Carroll, D. M., Meredith, K., Andersen, M. S., Brügger, C., Huang, H., et al. (2020). Changes in groundwater dissolved organic matter character in a coastal sand aquifer due to rainfall recharge. Water Research 169, 115201. doi:10.1016/j.watres.2019.115201

CrossRef Full Text | Google Scholar

McTigue, N. D., Walker, Q. A., and Currin, C. A. (2021). Refining estimates of greenhouse gas emissions from salt Marsh “Blue Carbon” erosion and decomposition. Front. Mar. Sci. 8, 661442. doi:10.3389/fmars.2021.661442

CrossRef Full Text | Google Scholar

Morán, X. A. G., and Estrada, M. (2002). Phytoplanktonic DOC and POC production in the Bransfield and Gerlache Straits as derived from kinetic experiments of 14C incorporation. Deep Sea Res. Part II Top. Stud. Oceanogr. 49 (4-5), 769–786. doi:10.1016/S0967-0645(01)00123-0

CrossRef Full Text | Google Scholar

Murphy, D. V., Macdonald, A. J., Stockdale, E. A., Goulding, K. W. T., Fortune, S., Gaunt, J. L., et al. (2000). Soluble organic nitrogen in agricultural soils. Biol. Fertil. Soils 30 (5), 374–387. doi:10.1007/s003740050018

CrossRef Full Text | Google Scholar

Ran, L., Lu, X. X., and Xin, Z. (2014). Erosion-induced massive organic carbon burial and carbon emission in the Yellow River basin, China. Biogeosciences 11 (4), 945–959. doi:10.5194/bg-11-945-2014

CrossRef Full Text | Google Scholar

Ran, L., Tian, M., Fang, N., Wang, S., Lu, X., Yang, X., et al. (2018). Riverine carbon export in the arid to semiarid Wuding River catchment on the Chinese Loess Plateau. Biogeosciences 15 (12), 3857–3871. doi:10.5194/bg-15-3857-2018

CrossRef Full Text | Google Scholar

Rao, Z., Guo, W., Cao, J., Shi, F., Jiang, H., and Li, C. (2017). Relationship between the stable carbon isotopic composition of modern plants and surface soils and climate: a global review. Earth-Science Rev. 165, 110–119. doi:10.1016/j.earscirev.2016.12.007

CrossRef Full Text | Google Scholar

Rovira, A., and Greacen, E. (1957). The effect of aggregate disruption on the activity of microorganisms in the soil. Aust. J. Agric. Res. 8 (6), 659–673. doi:10.1071/AR9570659

CrossRef Full Text | Google Scholar

Schultz, P., and Urban, N. R. (2008). Effects of bacterial dynamics on organic matter decomposition and nutrient release from sediments: a modeling study. Ecol. Model. 210 (1-2), 1–14. doi:10.1016/j.ecolmodel.2007.06.026

CrossRef Full Text | Google Scholar

Sinsabaugh, R. L., Turner, B. L., Talbot, J. M., Waring, B. G., Powers, J. S., Kuske, C. R., et al. (2016). Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86 (2), 172–189. doi:10.1890/15-2110.1

CrossRef Full Text | Google Scholar

Smoak, J. M., Breithaupt, J. L., Smith, T. J., and Sanders, C. J. (2013). Sediment accretion and organic carbon burial relative to sea-level rise and storm events in two mangrove forests in Everglades National Park. CATENA 104, 58–66. doi:10.1016/j.catena.2012.10.009

CrossRef Full Text | Google Scholar

Song, X., Dong, J., Wang, H., Xie, H., Yu, Y., Geng, L., et al. (2023). Factors influencing the distribution of organic carbon in four different coastal sedimentary environments. J. Soils Sediments 23 (3), 1539–1551. doi:10.1007/s11368-022-03423-5

CrossRef Full Text | Google Scholar

Steen, A. D., Crits-Christoph, A., Carini, P., DeAngelis, K. M., Fierer, N., Lloyd, K. G., et al. (2019). High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 13 (12), 3126–3130. doi:10.1038/s41396-019-0484-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, F., Hu, W., Wang, X., Cao, J., Fu, B., Wu, H., et al. (2021). Methanogen microfossils and methanogenesis in Permian lake deposits. Geology 49 (1), 13–18. doi:10.1130/G47857.1

CrossRef Full Text | Google Scholar

Varma, K., Jha, P. K., Mukherjee, S., Singhal, A., and Kumar, M. (2022). Provenances, preponderances, and distribution of humic acids and organic pollutants in hydro-geosphere: the co-existence, interaction and isotopic biomarkers in the riverine ecosystem. J. Environ. Manag. 313, 114996. doi:10.1016/j.jenvman.2022.114996

PubMed Abstract | CrossRef Full Text | Google Scholar

Vermeer, A., Van Riemsdijk, W., and Koopal, L. (1998). Adsorption of humic acid to mineral particles. 1. Specific and electrostatic interactions. Langmuir 14 (10), 2810–2819. doi:10.1021/la970624r

CrossRef Full Text | Google Scholar

Walks, D. (2007). Persistence of plankton in flowing water. Can. J. Fish. Aquatic Sci. 64 (12), 1693–1702. doi:10.1139/f07-131

CrossRef Full Text | Google Scholar

Wang, L., Liu, W., Zhou, X., Fu, S., Yang, P., Tong, C., et al. (2025). Effects of plant invasion and land use change on soil labile organic carbon in southern China’s coastal wetlands. Soil Ecol. Lett. 7 (1), 240275–11. doi:10.1007/s42832-024-0275-x

CrossRef Full Text | Google Scholar

Wang, L., Ying Rong, Y. R., Shi JiaQi, S. J., Long Tao, L. T., and Lin YuSuo, L. Y. (2017). Advancement in study on adsorption of organic matter on soil minerals and its mechanism[J]. Acta Pedologica Sinica 54 (4), 805–818. doi:10.11766/trxb201611040406

CrossRef Full Text | Google Scholar

Wen, Z., Shang, Y., Song, K., Liu, G., Hou, J., Lyu, L., et al. (2022). Composition of dissolved organic matter (DOM) in lakes responds to the trophic state and phytoplankton community succession. Water Res. 224, 119073. doi:10.1016/j.watres.2022.119073

PubMed Abstract | CrossRef Full Text | Google Scholar

Wen, Z., Song, K., Liu, G., Lyu, L., Shang, Y., Fang, C., et al. (2020). Characterizing DOC sources in China’s Haihe River basin using spectroscopy and stable carbon isotopes. Environ. Pollut. 258, 113684. doi:10.1016/j.envpol.2019.113684

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, W. Y., Ma, J. S., Yang, L., Li, M., and Tang, J. F. (2023). Distribution of biodegradable dissolved organic matter and its affecting factors in a typical Peri-urban watershed in Yangtze River Delta. Huan Jing Ke Xue 44 (1), 210–218. doi:10.13227/j.hjkx.202203064

PubMed Abstract | CrossRef Full Text | Google Scholar

Xia, X., Yang, Z., and Zhang, X. (2009). Effect of suspended-sediment concentration on nitrification in River water: importance of suspended sediment−water interface. Environ. Sci. Technol. 43 (10), 3681–3687. doi:10.1021/es8036675

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, Q.-F., Xia, Y., Li, S.-J., Wang, W.-Z., and Li, Z. (2023). Temporal and spatial distribution characteristics and source analysis of nitrate in surface water of Wuding River basin. Huanjing Kexue 44 (6), 3174–3183. doi:10.13227/j.hjkx.202207291

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, L., Xie, X., Heiss, J. W., Peng, K., Deng, Y., Gan, Y., et al. (2023). Isotopic and spectral signatures unravel the sources, preservation and degradation of sedimentary organic matter in the Dongzhai Harbor mangrove estuary, southern China. J. Hydrology 618, 129256. doi:10.1016/j.jhydrol.2023.129256

CrossRef Full Text | Google Scholar

Yang, C., Li, Z., Wang, S., Ran, F., Nie, X., Liu, Y., et al. (2023). Anthropogenic activities control the source dynamics of sediment organic carbon in the lower reach of an inland river. Water Res. 233, 119779. doi:10.1016/j.watres.2023.119779

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, Q., Gao, H., Han, Y., Li, Z., and Lu, K. (2024). Coupling changes in runoff and sediment and their relationships with erosion energy and underlying surface in the Wuding River Basin, China. Land 13 (4), 496. doi:10.3390/land13040496

CrossRef Full Text | Google Scholar

Yanling, Z., and Zhijun, X. (2019). Assessment of water quality of wuding River Basin in Yulin City. Agric. Eng. 9 (2), 31–33.

Google Scholar

Yu, K., Zhang, Y., He, X., Zhao, Z., Zhang, M., Chen, Y., et al. (2022a). Characteristics and environmental significance of organic carbon in sediments from Taihu Lake, China. Ecol. Indic. 138, 108796. doi:10.1016/j.ecolind.2022.108796

CrossRef Full Text | Google Scholar

Yu, X.-Q., Meng, X.-Q., Wu, H.-W., Chen, H.-M., Li, Y.-Y., Zhu, J.-Y., et al. (2022b). Source and optical dynamics of chromophoric dissolved organic matter in the watershed of Lake Qinghai. Huanjing Kexue 43 (2), 826–836. doi:10.13227/j.hjkx.202105164

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhan, J., Li, Y., Zhao, X., Yang, H., Ning, Z., and Zhang, R. (2023). Effects of nitrogen addition and plant litter manipulation on soil fungal and bacterial communities in a semiarid sandy land. Front. Microbiol. 14, 1013570. doi:10.3389/fmicb.2023.1013570

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, A., Lv, W., Shu, Q., Chen, Z., Du, Y., Ye, H., et al. (2024a). Distribution characteristics and main influencing factors of organic carbon in sediments of Spartina Alterniflora wetlands along the Northern Jiangsu Coast, China. Land 13 (6), 741. doi:10.3390/land13060741

CrossRef Full Text | Google Scholar

Zhang, L.-Z., Xing, S.-p., Huang, F.-Y., Xiu, W., Rensing, C., Zhao, Y., et al. (2024b). Metabolic coupling of arsenic, carbon, nitrogen, and sulfur in high arsenic geothermal groundwater: evidence from molecular mechanisms to community ecology. Water Res. 249, 120953. doi:10.1016/j.watres.2023.120953

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, S., Liu, P., Zhang, S., McLaughlin, N. B., Jia, S., Huang, D., et al. (2022a). Contribution of rhizodeposit associated microbial groups to SOC varies with maize growth stages. Geoderma 422, 115947. doi:10.1016/j.geoderma.2022.115947

CrossRef Full Text | Google Scholar

Zhang, Z.-B., Duan, Y.-P., Tu, Y.-J., Luo, P.-C., and Gao, J. (2022b). Distribution characteristics, source analysis, and pollution evaluation of organic matter in surface sediments of Qingpu District, Yangtze River Delta integration demonstration area. Huanjing Kexue 43 (6), 3066–3076. doi:10.13227/j.hjkx.202107175

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, B., Yao, P., Bianchi, T. S., Wang, X., and Yu, Z. (2023). Contrasting controls of particulate organic carbon composition and age from riverine to coastal sediments of Eastern China Marginal Seas. Chem. Geol. 624, 121429. doi:10.1016/j.chemgeo.2023.121429

CrossRef Full Text | Google Scholar

Zhou, Y., Zhou, L., Zhang, Y., Garcia de Souza, J., Podgorski, D. C., Spencer, R. G. M., et al. (2019). Autochthonous dissolved organic matter potentially fuels methane ebullition from experimental lakes. Water Res. 166, 115048. doi:10.1016/j.watres.2019.115048

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, E., Liu, Z., Ma, L., Luo, J., Kang, E., Wang, Y., et al. (2024). Enhanced mineral preservation rather than microbial residue production dictates the accrual of mineral-associated organic carbon along a weathering gradient. Geophys. Res. Lett. 51 (6), e2024GL108466. doi:10.1029/2024GL108466

CrossRef Full Text | Google Scholar

Zhuang, D., Shao, T., and Zheng, K. (2023). Characteristics, sources and driving factors of riverine CDOM in a severe erosion basin on the Loess Plateau, China. Ecol. Indic. 148, 110080. doi:10.1016/j.ecolind.2023.110080

CrossRef Full Text | Google Scholar