The source region of the Yellow River is situated in the eastern QTP (Fig. 1a), covering approximately 133 000 km², with elevations ranging from 2650 to 6250 m. The mean annual temperature in this area ranges between -3.5 and 7.5 °C. The annual average precipitation in this area ranges from 420 to 705 mm, with the highest values occurring in the midstream region (Yang et al., 2023). The dominant land cover consists of alpine meadows and alpine steppes (Fig. S1). The area covers extensive permafrost and seasonally frozen ground, together covering more than 85 % of the region (Li et al., 2025e; Yang et al., 2025). Under the combined influence of climate warming and anthropogenic disturbances, permafrost in the upper reaches of the Yellow River source region has shown a significant trend of degradation in recent decades (Yang et al., 2023). The headwater region of the Yellow River (defined as upstream Xunhua (M4) in this study, which is located on the edge of the Tibetan Plateau) contains a dense network of tributaries with annual average discharge of 1.85 × 10¹⁰ m³ during 1987–2016. Because damming affects the hydrology downstream of Tangnaihai (M2), the discharge at M2 is considered representative of the study area's natural discharge in our study. Within the national surface water quality monitoring network, four sites (M1–M4, Maqu, Tangnaihai, Longyangxia, and Xunhua) are situated at the headwaters of the Yellow River (Fig. 1b). In this study, the upstream (source to Deang), midstream (Deang to Longwu), and downstream sections (Longwu to Xunhua) of the Yellow River's source region are divided based on their geographical characteristics and are labeled B1(source-C1), B2 (C1–C2), and B3 (C2–C3) (Fig. 1).

To predict future COD_Mn levels at 160 sites in the headwaters of the Yellow River, the machine learning approach was also employed to model the relationships between geographical variables and the satellite-based estimates of COD_Mn levels. Soil texture (sand, silt, clay), bulk density, soil organic carbon, soil pH, altitude, human activity intensity, vegetation cover, land use, and ten-year averages of annual mean precipitation and temperature for each site during 2014–2024 were used as input features, while the ten-year average COD_Mn concentration for each site during 2014–2024 served as the target variable. The best-performing model, selected based on test and validation scores, was then used to predict future COD_Mn concentrations, incorporating both static variables (e.g., soil properties, vegetation cover, land use) and dynamic variables (future annual mean precipitation and temperature), depending on data availability. The projected future COD_Mn was model-based scenario projections conditional on the spatial stationarity assumption rather than independently validated forecasts. Therefore, the selected model was used to learn the spatial relationships between geographical/environmental characteristics and Landsat-derived COD_Mn estimates, rather than directly learning an observed COD_Mn response. Satellite-based COD_Mn estimates, rather than directly observed COD_Mn measurements were used in this model, prediction uncertainty from the first-stage remote-sensing model can propagate into this model.

In order to predict the future COD_Mn using the trained model, future monthly temperature and precipitation data was collected from Hu et al. (2025). The bias correction was performed by using the delta change method (Sect. S1) to reconcile differences in temperature and precipitation data sources (Beyer et al., 2020; Cao et al., 2026). Future annual mean precipitation and temperature were then calculated for 2025–2040, 2041–2070, and 2071–2100, representing near-, mid-, and long-term horizons. Future COD_Mn changes in the Yellow River source region for 2025–2040, 2041–2070, and 2071–2100 under SSP126, SSP245, and SSP585 scenarios were calculated relative to the mean values from 2014–2024.

The COD_Mn flux in the source region of the Yellow River was calculated using Eq. (4): 4F=Ci×Qi/10 where, F (Gg), nutrient export; Ci (mg L⁻¹), COD_Mn concentration at site i; Qi (10⁸ m³), river discharge at site i. The DOC flux was calculated from the DOC–COD_Mn relationship in rivers across China, expressed as 1.32F (Shang et al., 2026).

The developed model estimated an annual mean COD_Mn concentration of 2.14 ± 1.06 mg L⁻¹ in the Yellow River source region from 2014 to 2024, with pronounced spatial variability. In the upstream area, the mean COD_Mn was 1.89 ± 0.81 mg L⁻¹, while higher concentrations were observed in the mid-downstream region (2.73 ± 1.63 mg L⁻¹). The downstream area, which includes large reservoirs such as Longyangxia and Lijiaxia, exhibited significantly lower COD_Mn levels (1.54 ± 0.50 mg L⁻¹) (Fig. 5a, b).

Over the past decade, the midstream region showed a clear increasing trend in COD_Mn, with an average rise of 0.58 mg L⁻¹ (Fig. 5c, d). In contrast, the upstream area exhibited both increasing and decreasing trends, with a mean change of 0.08 mg L⁻¹, whereas COD_Mn concentrations in the downstream region remained relatively stable (mean change: -0.10 mg L⁻¹). However, the historical COD_Mn trend was not statistically significant in the headwater region of the Yellow River (Fig. S3).

Seasonal variations of COD_Mn were also evident during our study. Average monthly COD_Mn concentrations ranged from 0.8 to 8.0 mg L⁻¹ (Fig. 6). The downstream site maintained low (0.8–2.4 mg L⁻¹) and showed higher COD_Mn levels in summer (Fig. 6a), while the midstream site exhibited a pronounced increase from March, peaking in July (∼ 8 mg L⁻¹), followed by a gradual decline (Fig. 6b). The upper–midstream site displayed two distinct peaks in late spring and summer, suggesting episodic organic inputs associated with ice melt and rainfall (Fig. 6c). Upstream sites remained low to moderate (1–3 mg L⁻¹) with minimal seasonal variation (Fig. 6d).

Across all scenarios and periods, precipitation exhibits a consistent spatial pattern, increasing from upstream reaches to a midstream maximum and subsequently decreasing toward downstream sections (Fig. 7a–c). This pattern is preserved under all SSPs, although the magnitude of precipitation varied among scenarios and time slices. Relative to current conditions, precipitation increases slightly in the midstream region under SSP126 and SSP245, particularly during the mid- and late-century periods. Under SSP585, precipitation displays enhanced increase, with higher midstream values during 2041–2070 and 2071–2100 and lower precipitation toward downstream reaches.

Compared with current conditions, future temperature rises across the entire study area (Fig. 7d–f), with the magnitude of warming increasing over time and from SSP126 to SSP585. Under SSP126, temperature increases are relatively moderate, whereas SSP245 shows stronger warming, especially during 2071–2100. The largest temperature increases occurred under SSP585, with pronounced downstream variability during the late-century period, resulting in an amplified longitudinal temperature gradient.

Projected changes in COD_Mn across the study area show pronounced spatial and temporal variability under all three scenarios (Fig. 8). During the near-term period (2025–2040), COD_Mn changes are generally modest, with most monitoring sites exhibiting values close to zero. In the mid-term period (2041–2070), the COD_Mn changes show an overall increase, with mean value of 0.05 mg L⁻¹. Specifically, midstream sites more frequently experience decreases in COD_Mn, whereas upstream and downstream regions display a mixture of small increases and decreases. By the late-century period (2071–2100), the average change in COD_Mn is -0.01 mg L⁻¹. COD_Mn shows a pronounced decrease trend in the midstream area (-0.12 mg L⁻¹) under SSP585. Upstream and downstream areas show a higher occurrence of positive COD_Mn changes. Overall, COD_Mn shows a slight increasing trend across the three scenarios over different periods, with 877 positive changes (61 %) and 563 negative changes (39 %).

Historical and future COD_Mn and converted DOC exports are shown in Figs. 9 and S4, respectively. Based on the annual average discharge at control site M4 (1987–2021), approximately 28.1 Gg of COD_Mn (37.1 Gg DOC) was exported from the entire study area. In contrast, the annual mean COD_Mn export at site M2 was 62.5 Gg (82.5 Gg DOC), indicating that approximately 34 Gg of COD (44.8 Gg DOC) was either deposited or decomposed between M4 and M2 along the downstream of the Yellow River's source region. The export of riverine organic matter primarily occurred during the wet season (June–October) (Figs. 9a, S4a), which contributes substantially to the annual dissolved organic matter export from the source region (Figs. 9b, S4b). Additionally, the observed increase in COD_Mn or DOC export between sites M1 and M2 highlights the midstream region as a major source of dissolved organic matter (Figs. 9b, S4b).

Though the projected discharge presents an overall increase trend (Fig. 9c), the calculated future COD_Mn (or DOC) export at site M2 indicates a slight declining trend under the three scenarios (Figs. 9d, S4c). For example, the average COD_Mn export at M2 under different scenarios is expected to decrease from 62.5 to 59.6 Gg (82.5 to 78.7 Gg DOC) by 2100.

Rapid climate change in alpine regions is altering carbon cycling in both terrestrial and aquatic ecosystems (Chen et al., 2022). As carbon dynamics are highly sensitive to climatic variations, warming and wetting trends are expected to influence the stability of riverine organic matter in alpine catchments. Numerous studies indicate that precipitation and temperature on the QTP are projected to increase throughout this century, particularly in high-elevation areas (Meng et al., 2023; Karim et al., 2025). Our projections also suggest an overall increase in precipitation, especially in the up-midstream area of the Yellow River source region (Fig. 7), and significant warming is projected in the whole study area by 2100, especially under SSP245 and SSP585. Climate-driven changes are expected to induce the largest variability in COD_Mn concentrations in the midstream region (Fig. 8). COD_Mn levels are projected to increase in most parts of the source region of the Yellow River, whereas annual average COD_Mn exports from the whole catchment will decrease slightly from 62.5 to 59.6 Gg (82.5 to 78.7 Gg DOC) (Figs. 9d, S4c).

ROM dynamics are jointly regulated by organic matter supply and hydrological processes within catchments (Li et al., 2022; Wu et al., 2024). Increased precipitation can enhance surface runoff, leading to losses of organic matter from the catchment, and COD_Mn concentrations may be diluted as discharge continues to increase. The projected warming in the headwater area of the Yellow River will lead to the continuing permafrost degradation, promoting the loss of organic matter within the catchment and leading to the increase of the COD_Mn level in the headstreams. In addition, damming prolongs water residence time, promoting organic matter deposition in cascade reservoirs in the downstream area of the Yellow River's source region and reducing the riverine organic matter export from the study area. Under warm conditions, the degradation of deposited organic matter in the bottom of the reservoir may be enhanced, releasing additional organic carbon from reservoir sediments and contributing to the rise of COD_Mn levels in the downstream area (Vachon et al., 2021; Battin et al., 2023).

However, the extent to which organic matter reaches rivers depends on hydrological processes and connectivity between the source region and river channels. The characteristics of rainfall events may shift the ROM response to storms from transport-limited to supply-limited, thereby reducing ROM levels (Li et al., 2022). Consequently, the projected increase in precipitation by the year of 2100 is expected to lead to a decrease in COD_Mn levels within the midstream areas (Fig. 8). In addition, permafrost thaw induced by the warming can also alter runoff pathways, promoting subsurface flow and potentially reducing surface discharge to rivers (Yang et al., 2023). Increased vegetation cover can also reduce transport of organic matter through reducing soil erosion and surface runoff (Ma et al., 2023b; Liu et al., 2024). Therefore, the export of DOC from the headwaters of the Yellow River may remain stable or decrease slightly under the combined effects of climate change, hydrological variability, permafrost degradation, and human activities.

Understanding riverine organic matter dynamics is essential for assessing aquatic ecosystem feedback to climate change. Ongoing warming and wetting on the QTP are expected to alter carbon stability, increasing uncertainty in watershed organic matter losses (Bai et al., 2025). For instance, increased precipitation may enhance surface runoff, however, rising temperatures can also decrease catchment runoff by enhancing evaporation. The influence of hydrological processes on ROM dynamics depends on the characteristics of the catchment (Li et al., 2022). Previous studies have predicted both increasing and decreasing streamflow trends on the QTP under future climate scenarios (Yang et al., 2023; Li et al., 2025a; San et al., 2025). Increased streamflow may elevate COD_Mn in rivers, or dilute COD_Mn due to higher streamflow. Conversely, reduced streamflow may decrease organic matter transport to rivers or concentrate COD_Mn in river water.

Additionally, warming-induced permafrost thaw may accelerate topsoil organic matter mobilization, stimulate microbial activity, and promote soil organic matter decomposition, releasing more dissolved organic matter (Li et al., 2025d). However, the frozen ground degradation can also result in the decreased surface runoff by increasing the subsurface runoff (Yang et al., 2023). Vegetation greening on the QTP may both enhance soil carbon storage and reduce organic matter losses to river networks through mitigating soil erosion (Deng et al., 2025). Therefore, climate change can further differentially affect terrestrial carbon stocks and carbon losses, introducing additional complexity and uncertainty in catchment-scale organic matter sources and transport (Chen et al., 2022; Ma et al., 2023b; Liu et al., 2024).

In-stream carbon biogeochemical processes are also highly complex under changing climatic conditions. Organic matter decomposition depends on temperature, redox conditions, hydrology, microbial activity, and organic matter characteristics (Battin et al., 2023). The climate change is projected to change the hydrological processes and the associated biogeochemical processes, further altering the carbon cycling in the alpine rivers. However, interactions among these factors are not fully understood in alpine rivers, making predictions of alpine river metabolism and carbon budget under climate change uncertain (Li et al., 2025c).

Anthropogenic activities on the QTP further contribute to uncertainty. Warming and wetting may expand farming, increasing soil erosion and carbon losses (Chen et al., 2022). Future dam construction alters organic matter transport by promoting deposition, influencing greenhouse gas emissions, and facilitating organic matter degradation (Battin et al., 2023). Conversely, ecological restoration, such as afforestation, can increase soil organic matter accumulation and litterfall (Chen et al., 2022; Yan et al., 2023), leading to increase in organic matter and the COD_Mn levels in the alpine Rivers.

In this study, we project a general increase in levels and fluxes of COD_Mn based on modeled temperature, precipitation, and discharge. However, future climatic trends may deviate from current projections, and the predicted temperature and precipitation may not represent the actual future patterns. Besides, our developed model does not incorporate changes in land covers, soil carbon stocks, hydrological processes, or in-stream carbon biogeochemistry, all of which may significantly influence riverine carbon cycling. Therefore, future research should integrate these factors to develop more precise, process-based models and improve understanding of carbon dynamics in alpine river catchments.

In addition, future COD_Mn projection model is trained on COD_Mn values predicted by the first-stage retrieval model rather than on independent long-term observations, so the two models are not statistically independent and retrieval errors inevitably propagate into the climate projections. The extrapolating spatial “geographical characteristics – COD_Mn concentration” relationships to temporal climate change represents a space-for-time substitution that assumes stationarity, an assumption that is difficult to test rigorously with the available data. Finally, COD_Mn is an oxygen-equivalent index rather than a direct measure of organic matter or carbon. The DOC export estimated in this study was derived from a simplified empirical relationship between COD_Mn and DOC in rivers across China, which may introduce additional uncertainty into the DOC estimates.