Rivers as the link between terrestrial ecosystems and oceans have been demonstrated to transport a large amount of dissolved organic carbon (DOC) to downstream ecosystems. In the source region of the Yellow River (SRYR), climate warming has resulted in the rapid retreat of glaciers and permafrost, which has raised discussion on whether DOC production will increase significantly. Here, we present three-year data of DOC concentrations in river water and precipitation, explore the deposition and transport processes of DOC from SRYR. Results show that annual mean concentrations of riverine DOC ranged from 2.03 to 2.34 mg/L, with an average of 2.21 mg/L. Its seasonal variation is characterized by the highest concentration in spring and summer (2.65 mg/L and 2.62 mg/L, respectively), followed by autumn (1.95 mg/L), and the lowest in winter (1.44 mg/L), which is closely related to changes in river runoff under the influence of precipitation and temperature. The average concentration of DOC in precipitation (2.18 mg/L) is comparable with riverine DOC, while the value is inversely related to precipitation amount and is considered to be the result of precipitation dilution. DOC deposition flux in precipitation that is affected by both precipitation amount and DOC concentration roughly was 86,080, 105,804, and 73,072 tons/year from 2013 to 2015, respectively. DOC flux delivered by the river ranged from 24,629 to 37,539 tons/year and was dominated by river discharge. Although permafrost degradation in SRYR is increasing, DOC yield is not as significant as previously assumed and is much less than other large rivers in the world.

The carbon release and transport in rivers are expected to increase in a warming climate with enhanced melting. We present a continuous dataset of DOC in the river, precipitation, and groundwater, including air temperature, discharge, and precipitation in the source region of the Yangtze River (SRYR). Our study shows that the average concentrations of DOC in the three end-members are characterized as the sequence of groundwater > precipitation > river, which is related to the water volume, cycle period, and river flow speed. The seasonality of DOC in the river is observed as the obvious bimodal structure at Tuotuohe (TTH) and Zhimenda (ZMD) gauging stations. The highest concentration appears in July (2.4 mg L⁻¹ at TTH and 2.1 mg L⁻¹ at ZMD) and the secondary high value (2.2 mg L⁻¹ at TTH 1.9 mg L⁻¹ at ZMD) emerges from August to September. It is estimated that 459 and 6751 tons of DOC are transported by the river at TTH and ZMD, respectively. Although the wet deposition flux of DOC is nearly ten times higher than the river flux, riverine DOC still primarily originates from soil erosion of the basin rather than precipitation settlement. Riverine DOC fluxes are positively correlated with discharge, suggesting DOC fluxes are likely to increase in the future. Our findings highlight that permafrost degradation and glacier retreat have a great effect on DOC concentration in rivers and may become increasingly important for regional biogeochemical cycles.

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

The summer monsoon transition zone (SMTZ) in China represents an unusual land type with an agro-pasture ecotone, and it is a climate-sensitive region. Changes in climate aridity and changes in meteorological drought are mutually related yet fundamentally different. In this study, potential evapotranspiration (E_TO) is calculated using Penman–Monteith, based on China’s national meteorological stations data from 1961 to 2013. An E_TO-based climate aridity index (I_AI) and E_TO-based standardized precipitation evapotranspiration index (SPEI) are used as the metrics for climate aridity and meteorological drought, respectively. The result shows a significant difference between climate aridity and meteorological drought in the SMTZ, compared with the monsoon and non-monsoon zone. This difference varies on different time scales (1–48 months), and the greatest differences between I_AI and SPEI are on seasonal and monthly scales (1–12 months), but lower at longer time scales (>12 months). The first reason for the difference is the desynchronicity of meteorological drought and the background climate. After the background climate becomes a relatively arid state (such as 0.96 < I_AI < 1) from a semi-arid state (0.50 < I_AI < 0.80), the continued arid state with weak I_AI fluctuations eventually results in increasingly severe meteorological droughts, or the recurrence of equally severe droughts with drastic reduction. Consequently, the onset of the most severe climate aridity is two to seven months (mostly three to four months) ahead of the onset of the most severe drought events, until the climate returns to a semi-arid state. Second, climate aridity represents the average state of the background climate over a long time period and changes gently, while meteorological droughts are stochastic climate events and change drastically. These findings indicate that I_AI can serve as a predictor of the onset of meteorological drought events, especially in the SMTZ, but it fails to characterize the progression of meteorological drought events well. Therefore, this result is of great significance for drought prediction and early warning.