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The Haihe River Basin is a polluted area affected by the developing industry and intensive agricultural activities in China. Dissolved organic matter (DOC) and light-absorbing characteristics of chromophoric dissolved organic matter (CDOM) were monitored in different tributaries of China within the Haihe River basin during spring and autumn. The concentration of DOC during spring was higher than during autumn (p < 0.01), and the evaporation was an important factor affecting the concentration of DOC in the basin. By contrast, the proportion of inputs due to terrigenous plants during autumn was higher than during spring. Carbon stable isotope analysis δ 13 C and C: N ratio of DOC, evidenced the inputs of DOC in the Haihe River basin from different sources including sewage, terrestrial plants, soil, and plankton. Isotopic analysis of δ 13 C and excitation-emission matrix (EEM) with fluorescence regional integration (FRI) analysis supported the hypothesis that allochthonous inputs contributed substantially to the inputs of DOC in the Haihe River basin, coming largely from sewage (9.8%-81.2%) and terrestrial plants (13.3%-65.8%). Depending on the source of DOC and contribution, four types with different EEM spectra were set. Type I, river water from sewage (81.2%); Type II, river water with input from terrestrial plants (65.8%); Type III, river water with plankton (36.4%), and Type IV, river water with soil-derived DOC (33.9%). The results demonstrated that the combined methodology using 13 C stable isotope and EEM-FRI can be used to characterize the components of DOC in river waters. This approach was important for tracking the concentration and composition of DOC in river waters from different input sources and for better understanding concerning the local regulation of the terrestrial carbon cycle., Competing Interests: Declaration of competing interest No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. All authors read and approved the final manuscript. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In this study, the dissolved organic carbon (DOC) concentrations, chromophoric dissolved organic matter (CDOM) absorption coefficient a(254), and excitation-emission matrix fluorescence (EEM) were examined in the Songhua River (SHR) and its highly polluted tributary of Northeast China. Fluorescence regional integration (FRI) was used to identify five fluorescent regions: one tyrosine-like (R1), one tryptophan-like (R2), one fulvic-like (R3), one microbial by-product-like (R4), and one humic-like (R5) regions. The five EEM-FRI regions for all water samples have site-specific properties. Principle component analysis (PCA) was conducted to assess variations in the five FRI regions and the humification index (HIX) for all water samples. For the water samples from the mainstream of SHR, CDOM absorption coefficient a(254) was correlated with either DOC or FRI fluorescent regions (R3 and R5), respectively. FRI R3 region was also correlated with R5 region for the water samples in the mainstream of SHR. However, the determination coefficients (R 2 ) and slopes of these relationships among CDOM absorption, fluorescent regions, and DOC all decreased when the SHR waters were influenced by the highly polluted tributary of Yinma River (YMR) and Yitong River (YTR), which has a negative effect on the estimation of DOC flux transported by the SHR to oceans.

As a major fraction of carbon in inland waters, dissolved organic carbon (DOC) plays a crucial role in carbon cycling on a global scale. However, the quantity of DOC stored in lakes and reservoirs was not clear to date. In an attempt to examine the factors that determine the DOC storage in lakes and reservoirs across China, we assembled a large database (measured 367 lakes, and meta-analyzed 102 lakes from five limnetic regions; measured 144 reservoirs, and meta-analyzed 272 reservoirs from 31 provincial units) of DOC concentrations and water storages for lakes and reservoirs that are used to determine DOC storage in static inland waters. We found that DOC concentrations in saline waters (Mean/median ± S.D: 50.5/30.0 ± 55.97 mg/L) are much higher than those in fresh waters (8.1/5.9 ± 6.8 mg/L), while lake DOC concentrations (25.9/11.5 ± 42.04 mg/L) are much higher than those in reservoirs (5.0/3.8 ± 4.5 mg/L). In terms of lake water volume and DOC storage, the Tibet-Qinghai lake region has the largest water volume (552.8 km 3 ), 92% of which is saline waters, thus the largest DOC (13.39 Tg) is stored in these alpine lake region; followed by the Mengxin lake region, having a water volume of 99.4 km 3 in which 1.75 Tg DOC was stored. Compared to Mengxin lake region, almost the same amount of water was stored in East China lake region (91.9 km 3 ), however, much less DOC was stored in this region (0.43 Tg) due to the lower DOC concentration (Ave: 3.45 ± 2.68 mg/L). According to our investigation, Yungui and Northeast lake regions had water storages of 32.14 km 3 and 19.44 km 3 respectively, but relatively less DOC was stored in Yungui (0.13 Tg) than in Northeast lake region (0.19 Tg). Due to low DOC concentration in reservoirs, especially these large reservoirs having lower DOC concentration (V > 1.0 km 3 : 2.31 ± 1.48 mg/L), only 1.54 Tg was stored in a 485.1 km 3 volume of water contained in reservoirs across the entire country., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change. [ABSTRACT FROM AUTHOR]

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1‐m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being.65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m−2 in 2000 and 26.57 ± 3.73 kg C m−2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m−2 year−1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1‐2.6 (low‐carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5‐8.5 (high‐carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage. [ABSTRACT FROM AUTHOR]

[Display omitted] • Forest and grassland are the main types of land conversion for montane soils. • Land conversion to cropland led to the decrease of soil organic carbon. • The slope of current land conversion to cropland is higher than that of before. Soil organic carbon (SOC) plays important roles in soil ecological function, soil conservation, and the global carbon cycle. With the increase of population and food demand, more and more croplands were reclaimed in mountainous areas. However, land conversion to cropland can lead to evident changes of SOC. The innovation of this study was to quantify the changes of SOC for different land covers in montane soils. Eight hundred twenty-nine topsoil samples were collected from dryland in Northeast China, and each soil sample was collected at least 200 m off the road at every 5–10 km interval, and five sites were mixed as one soil sample. Landsat 8 images were acquired from the bare soil period, and land cover data for 1985–2020 were obtained from Yang and Huang (2021). The surface reflectance of the 829 topsoil samples (0–20 cm) was extracted from Landsat 8 images. Then, a SOC prediction hybrid model was built after k-means clustering based on measured SOC using the potassium dichromate heating method, and a probability hybrid model was developed for SOC mapping. Land conversion to cropland was assigned to eight periods (i.e., five-year intervals from 1985 to 2020) based on land cover data, and the conversion percentages of different land cover types to cropland were calculated. Seven mountainous sub-regions were selected in Northeast China based on distribution of cropland and mountains to analyze the effects of land conversion to cropland on SOC. Our study showed that (1) the hybrid model had a higher accuracy than the global model, and the hybrid model led to the R2 of 0.77 and RMSE of 4.66 g kg−1 for validation samples. (2) Land conversion to cropland was mainly from forest in the 1980 s (proportion of 67.13%) and from grassland after 2015 (proportion of 52.24%). (3) SOC decreases with an average rate of 0.30 g kg -1 per five years from 1985 to 2020, and the slope of land conversion to cropland increases with an average rate of 0.35° per five years from 1985 to 2020; (4) SOC of land conversion to cropland from forests decreased by 2.04 g kg−1, and wetland has an evident decrease trend (decrease by 1.84 g kg−1) and an increasing trend from barren (increase by 4.07 g kg−1). Our results suggested that land conversion to cropland has a negative effect on SOC in montane soils and should control land conversion to cropland to protect natural resources. [ABSTRACT FROM AUTHOR]

Understanding concentrations of carbon dioxide (CO 2 ) and methane (CH 4 ) in lakes is an important part of a comprehensive global carbon budget. We estimated data on the partial pressure of CO 2 ( p CO 2 ) and CH 4 ( p CH 4 ) from sampling with 95 lakes in semi-humid/semi-arid region of Northeastern China during ice-free period. Both p CO 2 and p CH 4 varied greatly among the study sites. p (CO 2 ) values in these lakes ranged from 21.9 to 30,152.3 μatm (n = 403), and 91% of lakes in this survey were supersaturated with CO 2 . p (CH 4 ) values ranged from 12.6 to 139,630.7 μatm with all sites in this study of CH 4 sources to the atmosphere during the ice-free period. The collected urban lakes samples exhibited higher p CO 2 and p CH 4 than wild lakes samples. Either the mean value of p (CO 2 ) or p (CH 4 ) in saline waters is higher than in fresh waters. Correlation analysis implied that the partial pressure of the GHGs (CO 2 and CH 4 ) showed statistically correlations with water environment indicators like pH, dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), and chlorophyll a ( Chla ). However, the most of the relationships showed a high degree of scatter, only pH might be used as the predictor of the gas partial pressure based on the result of this study ( r p CO2 = −0.437, p < 0.01, n = 382; r p CH4 = −0.265, p < 0.01, n = 400). Furthermore, salinity could be a good predictor for p (CO 2 ) and p (CH 4 ) in 83 freshwater lakes in our study ( r p CO2 = 0.365, r p CH4 = 0.323, p < 0.01, n = 348). The mean CO 2 flux increased with the decreasing lake area size. The calculated annual areal carbon emission rate is 560.2 g C m −2 from 95 lakes in Northeastern China. We could not extrapolate carbon emission from these lakes to the boreal region or a wider scale because of the change of environmental conditions. [ABSTRACT FROM AUTHOR]

The traditional field-based measurements of carbon dioxide (pCO2) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO2 variation of the entire lake. However, these field measurements can be used in the pCO2 remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO2 based on published data. The results indicate the significant daily and seasonal variations in pCO2 in lakes. Rivers and streams contain higher pCO2 than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO2 than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO2, it can differ in different inland water types in the same space-time. The estimation of CO2 fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y−1. This paper also reviews existing remote sensing models/algorithms used for estimating pCO2 in sea and coastal waters and presents some perspectives and challenges of pCO2 estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO2 and CO2 emissions from inland waters at the global scale, more reliable and universal pCO2 remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO2 variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO2/the CO2 concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO2 values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO2 in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO2 in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle. [ABSTRACT FROM AUTHOR]

The Yellow River is the second largest river in China. Carbon transport by the Yellow River has significant influence on riverine carbon cycles in Asia. During the wet season, the riverine carbon was mainly found in dissolved form, i.e., dissolved organic carbon (DOC), along the entire course of the river. The distinct spatial variations of DOC concentration were observed at different reaches of the mainstream (p < 0.01), while the highest mean DOC concentration was generally observed at midstream (4.13 ± 0.91 mg/L). Carbon stable isotope analysis δ13C and C: N ratio of DOC, evidenced the sources of DOC in headwater and upstream were primarily the terrestrial plants (94% and 61%), but it was changed to soil organic matter (SOM) in mid- and downstream (36% and 37%), and the contribution of sewage to DOC were also increased to 17% and 18%. In the whole mainstream of the Yellow River, water temperature (WT) had a significant impact on DOC concentration, and it could explain 67% of the DOC variance. However, in a large catchment, the driving mechanisms on the DOC variations in headwaters will not necessarily be those controlling DOC trends in downstream. The study firstly quantified, in headwater and upstream, the natural factors explained as much as 65% and 73% of the DOC variations, respectively. In mid- and downstream areas, DOC was significantly influenced by the amount of wastewater discharged by the industry and the use of chemical fertilizers (p < 0.05). These findings may facilitate a better assessment of global riverine carbon cycling and may help to reveal the importance of the balance between development and environmental sustainability with the changing DOC transport features in the Yellow River due to human disturbances. [Display omitted] • DOC concentrations differed in different Yellow River reaches. • The main sources of DOC in headwater and upstream were the terrestrial plants. • The main sources of DOC in mid- and downstream were soil organic matter. • Natural factors explained 65% and 73% of DOC variations in headwater and upstream. • DOC in mid- and downstream was influenced by anthropogenic activities with 11%–42%. [ABSTRACT FROM AUTHOR]

• Mean DOC concentration in closed lakes was higher than open lakes in Tibet Plateau. • SHEI had the most significant impact on lake DOC concentration in Tibet Plateau. • DOC concentration in Qinghai Lake was higher than the inflowing rivers. • Slope and SOM were responsible for the higher DOC in Qinghai lake than rivers. As a significant fraction of carbon in inland waters, dissolved organic carbon (DOC) plays a crucial role in carbon cycling at a global scale. Understanding the linkages between variations in DOC and its dominant factors in lakes is critical for estimating its concentration on a larger scale. This study characterized the concentrations of DOC in the lakes of Tibet Plateau and explored the major underlying influencing factors. The overall mean concentration of DOC in these closed lakes had a mean value of 21.80 ± 30.82 mg/L (mean ± S.D.) and was higher than open lakes (F = 174.1, p < 0.05). The potential drivers for the concentration of DOC in the lakes of Tibet Plateau were conducted by the correlation analysis of DOC with the observed landscape, water quality variables, and climatic factors. The multiple regression model showed that 78% of DOC concentrations across the lakes could be explained by Shannon evenness index (SHEI) of landscape, slope, and landscape dominance index (LDI), of which SHEI explained the most substantial variations for the concentration of DOC. The changes in the concentrations of DOC in river-lake interaction were analyzed in Qinghai Lake watershed. The concentration of DOC in the Qinghai Lake water was higher than the surrounding rivers. Slope and the density of soil organic matter (SOM) in the Qinghai Lake watershed were responsible for the higher DOC in lake than rivers. The landscape had a significant influence on DOC in the lakes, and the dominant factor was SHEI. The results observed from this investigation further supplement the information of DOC in the inland waters with plateau lakes. Further, this study is expected to improve the understanding of the transport of DOC in the river-lake ecosystem. [ABSTRACT FROM AUTHOR]

• FDOM were explored from 244 samples of lakes in TQP. • FRI was conduct to characterize FDOM of fresh and brackish lakes. • Integrating effect of evapoconcentration, ultraviolet irradiance and landscapes. • Unique environmental factors control the FDOM properties in TQP. Fluorescent dissolved organic matter (FDOM) has a significant influence on global inland water carbon cycling and biogeochemical processes. The alpine lakes on Tibet-Qinghai Plateau (TQP) are exposed to high-intensity ultraviolet radiation, long retention time with less anthropogenic influences. Yet, limited studies were conducted on FDOM of lakes on the highest plateau in the world with unique aquatic environments. Spatiotemporal variations of FDOM components from 63 lakes (32 fresh lakes, N = 135; 31 brackish lakes, N = 109) were grouped according to salinity across the TQP collected during 2014–2017. FDOM were examined using excitation-emission matrix spectra (EEM) and fluorescence regional integration (FRI). CDOM absorption and FDOM fluorescence indices, average fluorescence intensities of the five fluorescent components (φ i, i = I, II, III, IV, V) and total fluorescence intensities (φ T) are significantly different between brackish and freshwater lakes (p < 0.05). High correlations (−0.51** < r < −0.86**, p < 0.01) were revealed between FDOM and dissolved organic carbon (DOC) concentrations with salinity > 10‰. A comparatively prolonged retention time and terrestrial allochthonous inputs caused higher DOC accumulation in brackish water. Strong UV radiation resulted in an important effect on CDOM photo-absorption characteristics, and that contributed to DOC variability and fate. Likewise, average CDOM spectroscopic indices in each basin with different land covers showed a moderate correlation between DOC and normalized humic-like φ V for 20 basins (r = −0.46**, p < 0.01). The results suggest that studies on the highest alpine lakes across the TQP should focus on the impact of the evapo-concentration, ultraviolet irradiance and landscape features within different basins on FDOM characteristics. It demonstrated that the FDOM was dominated by the allochthonous sources in brackish lakes from the TQP and had a similarity to those observed in marine environments. [ABSTRACT FROM AUTHOR]

• Molecular formulas of DOM show a positive relationship with the trophic state index. • Nitrogen-containing compounds are the most abundant formulas in DOM. • Eutrophication modifies DOM structure into more heteroatom S-containing compounds. • Eutrophication may be a potential source of refractory DOM compounds. • Seasonal phytoplankton community succession affects the molecular lability of DOM. Dissolved organic matter (DOM), a heterogeneous mixture of diverse compounds with different molecular weights, is crucial for the lake carbon cycle. The properties and concentration of DOM in lakes are closely related to anthropogenic activities, terrigenous input, and phytoplankton growth. Thus, the lake's trophic state, along with the above factors, has an important effect on DOM. We determined the DOM sources and molecular composition in six lakes along a trophic gradient during and after phytoplankton bloom by combining optical techniques and the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). CDOM pools in eutrophic lakes may be more biologically refractory than in oligotrophic and mesotrophic lakes. Molecular formulas of DOM were positively correlated with the TSI (trophic state index) value (R 2 = 0.73), with the nitrogen-containing compounds (CHON) being the most abundant formulas in all studied lakes. Eutrophication modified the molecular formulas of DOM to have less CHO% and more heteroatom S-containing compounds (CHOS% and CHNOS%), and this was the synactic result of the anthropogenic perturbation and phytoplankton proliferation. In eutrophic lakes, summer DOM showed higher molecular lability than in autumn, which was related to the seasonal phytoplankton community succession. Although the phytoplankton-derived DOM is highly bioavailable, we detected a simpler and more fragile phytoplankton community ecosystem in autumn, which may be accompanied by a lower phytoplankton production and metabolic activity. Therefore, we concluded that the lake eutrophication increased the allochthonous DOM accumulation along with sewage and nutrient input, and subsequently increased its release with phytoplankton bloom. Eutrophication and phytoplankton growth are accompanied by more highly unsaturated compounds, O 3 S+O 5 S compounds, and carboxylic-rich alicyclic compounds (CRAMs), which are the biotransformation product of phytoplankton-derived DOM. Eutrophication may be a potential source of refractory DOM compounds for biodegradation and photodegradation. Our results can clarify the potential role of water organic matter in the future global carbon cycle processes, considering the increasing worldwide eutrophication of inland waters. [Display omitted] [ABSTRACT FROM AUTHOR]

• Optical properties and molecular diversity of DOM across Chinese lakes were analyzed. • Diverse DOM sources for humic-rich and protein-rich lakes across large-scale regions were firstly revealed. • Natural and anthropogenic factors affecting regional DOM chemistry were evaluated quantitatively. • Linkages between DOM optical properties and aquatic carbon parameters in Chinese lakes were established. Dissolved organic matter (DOM) plays an essential role in the global carbon biogeochemical cycle for aquatic ecosystems. The complexity of DOM compounds contributes to the accurate monitoring of its sources and compositions from large-scale patterns to microscopic molecular groups. Here, this study demonstrates the diverse sources and compositions for humic-rich lakes and protein-rich lakes for large-scale regions across China with the linkage to optical components and molecular high-resolution mass spectrometry properties. The total fluorescence intensity of colored DOM (CDOM) for humic-rich lake regions (0.176 Raman unit; R.U.) is significantly (p <0.05) higher than that of the protein-rich lake region (0.084 R.U.). The combined percentages of CDOM absorption variance explained by the anthropogenic and climatic variables across the five lake regions of Northeastern lake region (NLR), Yungui Plateau lake region (YGR), Inner Mongolia-Xinjiang lake region (MXR), Eastern lake region (ELR), and Tibetan-Qinghai Plateau lake region (TQR) were 86.25%, 82.57%, 80.23%, 88.55%, and 87.72% respectively. The averaged relative intensity percentages of CHOS and CHONS formulas from humic-rich lakes (90.831‰, 10.561‰) were significantly higher than that from the protein-like lakes (47.484‰, 5.638‰), respectively. The more complex molecular composition with higher aromaticity occurred in the humic-rich lakes than in the protein-rich lakes. The increasing anthropogenic effects would significantly enhance the sources, transformation, and biodegradation of terrestrial DOM and link to the greenhouse gas emission and the carbon cycle in inland waters. [Display omitted] [ABSTRACT FROM AUTHOR]

• Sentinel-2 images were used to modeling p CO 2 in the water of Songhua River. • XGBoost model was satisfactorily applied to map p CO 2 in the water of Songhua River. • XGBoost model has the potential to track CO 2 emissions from Songhua River. • Songhua River was a CO 2 sink with a net annual uptake of 15.6 Gg C. Rivers play a vital role in the global carbon cycle, even though they account for only 0.58 % of the Earth's non-glaciated land surface area. Owing to the significant spatial and temporal variability of the partial pressure of CO 2 (p CO 2) in river water, there is a large degree of uncertainty in the estimation of CO 2 flux from the river water-atmosphere interface. This study assembled more than 200 in-situ measured p CO 2 in the mainstream water of Songhua River and matched with Sentinel-2 overpasses within ± 7 days from the GEE platform. Multiple linear regression (MLR) and two machine learning models (Random Forest and XGBoost) algorithms were used to estimate p CO 2 in Songhua River. MLR, Random Forest, and XGBoost all demonstrated good performance and have the potential to map p CO 2 for different years using high-quality Sentinel-2 images. In all the tested modeling approaches, the XGBoost model exhibited stable performance in the validation set (R2 > 0.79, RMSE < 228 µatm, MAPE < 42.93 %, SMAPE < 30.64 %). The distribution of mapped p CO 2 by XGBoost model significantly varied with seasonal change, and the p CO 2 in summer (445 ± 125 µatm) was higher than in autumn (373 ± 58 µatm) and spring (351 ± 57 µatm). CO 2 emission flux from the mainstream water of Songhua River was calculated based on the inverse p CO 2 value with a net annual uptake of 15.6 Gg C during the ice-free period. The findings suggest that the Songhua River functions as a carbon sink during both spring and autumn but as a carbon source during the summer. In addition, the potential control of annual CO 2 flux spatial variability was attributed to the characteristics of the watershed landscapes. The study is critical to clarify the role of river systems in the global carbon cycle. [ABSTRACT FROM AUTHOR]

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change. • Lakes in taiga and tundra overall showed a decreased trend in browning from 2002 to 2021. • The browning trend of the study lakes varies significantly among ecoregions, temperature, precipitation, and lake area. • The results support the theory that climate change may cause increased DOC in lakes in pan-Arctic regions. [ABSTRACT FROM AUTHOR]

Abstract Freshwaters are important sources of greenhouse gases (GHGs) to the atmosphere that may partially offset the terrestrial carbon sink. However, current emission estimates from inland waters remain uncertain due to data paucity in key regions with a large freshwater surface area, such as China. Here, we show that the areal fluxes of GHGs (carbon dioxide, methane, and nitrous oxide) from lakes and reservoirs in China are much larger than previous estimates. Our work summarized data from 310 lakes and 153 reservoirs, and revealed diffusive emissions of 1.56 (95% confidence interval: 1.12–2.00) Tg C-CH 4 /y and 25.2 (20.8–29.5) Tg C-CO 2 /y from reservoirs and lakes. Chinese lakes and reservoirs emit 175.0 (134.7–215.3) Tg CO 2 equivalent, with 73.4% of this forcing contributed by lakes. These aquatic sources are equivalent to 14.1%–22.6% of China's estimated terrestrial carbon sink. Our results suggest a disproportionally high contribution of China's reservoirs and lakes to national and global GHGs emissions, highlighting major data gaps and the need of including more artificial and natural lakes data from developing countries like China in global GHGs budgets. Graphical abstract Image 1 Highlights • Much needed data for China's national lakes and reservoirs' GHG estimates are provided. • We provide magnitude and controls on GHGs emissions from China's lakes and reservoirs. • China's lake and reservoirs emit 1.56 Tg C-CH 4 /y and 25.2 Tg C-CO 2 /y. • China's lake and reservoirs disproportionately contribute to GHG fluxes. • The study advances of our standing of GHG budgets from China's inland waters. [ABSTRACT FROM AUTHOR]

• The mean soil organic carbon (SOC) content in agroecosystem of Eastern China was 18.24 g kg−1. • The SOC content of planted forest > cropland > grassland in the total study area. • The SOC value of grassland was higher than cropland in North China due to excessive mechanized operation. • The random forest hybrid model exhibited higher comprehensive performance in SOC prediction. • Our predictions were much more detailed and accurate than the existing SOC content maps. Soil organic carbon (SOC) plays a key role in soil function, ecosystem services, and the global carbon cycle. Large SOC stocks accumulate in agroecosystems, but the estimates of SOC distribution and magnitude in agroecosystems still have large uncertainties in global products. Based on multiple high-resolution environmental variables (terrain derived from digital elevation model, climate and organism from Landsat 8 OLI) and 369 observed soil samples from 2019 to 2021, two machine learning methods, random forest (RF) and support vector machine (SVM) models, were used to estimate the content and spatial distribution of SOC in agroecosystems of Eastern China. We proposed a hybrid model in which the optimal predictors of SOC were selected for different land use types (cropland, planted forest and grassland), and it aimed to improve the performance of machine learning. Our results showed that (1) the hybrid models performed better than the global models, and the Random forest-Hybrid (RF-Hybrid) model led to the highest prediction accuracy, with a validation R2 of 0.65 and RMSE of 5.76 g kg−1. (2) The SOC content in the agroecosystems of Eastern China among different land use types ranked in the following order: planted forest (19.92 g kg−1) > cropland (17.51 g kg−1) > grassland (17.30 g kg−1). However, in North China, the SOC content in cropland (14.04 g kg−1) was much lower than that in planted forest (17.21 g kg−1) and grassland (17.07 g kg−1), which was caused by excessive mechanized operation. (3) The agroecosystem in Northeast China presented the highest mean SOC content (26.55 g kg−1) due to low temperature. (4) Our estimations (R2 = 0.53, RMSE = 6.74 g kg−1, 30-m resolution) were more detailed and precise than the existing global SOC maps (Soil Grids: R2 = 0.26, RMSE = 12.44 g kg−1, 250-m resolution; HWSD: R2 = 0.06, RMSE = 41.07 g kg−1, 1000-m resolution). The results may improve the accuracy of agroecosystem carbon mapping and contribute to SOC assessments in agricultural ecosystems. [ABSTRACT FROM AUTHOR]

• Soil texture and climatic factors play key roles in digital soil class mapping. • Synthetic images improve the correlations of SOC to bands and band indices. • The mixed use of synthetic images is better than the use of single synthetic images. • The probability hybrid model has the high prediction accuracy and good mapping results. Soil organic carbon (SOC) plays a key role in soil function, ecosystem services, and the global carbon cycle. Digital SOC mapping is essential for agricultural production management. Digital SOC mapping based on multi-source remote sensing data has been integrated well into prediction models and methodological approaches on different mapping scales. However, the mixed use of synthetic images and the application of the hybrid model considering the soil classification probability are rare. Here, we propose a probability hybrid model to estimate and map SOC content and distribution. Multi-temporal synthetic images were used to build the probability hybrid model. One hundred forty topsoil samples were collected in Suihua, a city in a typical black soil region in China. Cloud-free Sentinel-2 images were acquired from the bare soil periods between 2018 and 2021. Minimum, maximum, mean, and median synthetic images were calculated using single images from the same period. The random forest and support vector machine models were used for discriminating soil class and calculating soil classification probability, and then random forest regression model was applied to SOC mapping. Soil class mapping and classification probability were performed for Phaeozems, Chernozems, and Cambisols of the World Reference Base for Soil Resources (WRB) based on soil texture, climatic factors, and the normalized differential vegetation index (NDVI). Based on soil class mapping results, global models using single temporal images and multi-temporal images, the hybrid model using multi-temporal images for the three soil classes as well as a probability hybrid model using multi-temporal images were built and compared their SOC predictions. Our study showed that (1) Phaeozems, Chernozems and Cambisols could be classified accurately, and the overall validation accuracy of random forest model was 91.67%. (2) The correlations of SOC with bands and band indices improved using multi-temporal images, and the use of mixed synthetic images was better than the use of only one synthetic image. (3) The hybrid model performed far better than the global models, and the probability hybrid model led to the highest prediction accuracy, a validation R2 of 0.77 and an RMSE of 2.30 g kg−1. (4) The probability hybrid model was more accurate than the original hybrid model for digital SOC mapping, and the SOC distribution in boundary regions was smoother and more continuous. Our results suggest that the probability hybrid model has a large potential for SOC prediction and mapping. [ABSTRACT FROM AUTHOR]

• DOM sources and composition of Chinese river estuaries were first analyzed. • Most of the DOM in estuaries came from the terrigenous sources (61.6%–79.2%). • Highly unsaturated compounds are the most abundant molecular formulas in river DOM. • The contribution of sewage to Chinese riverine DOM was estimated as 16.1–17.7 Gg C/yr. • First linking the molecular composition of DOM with its source across Chinese rivers. Rivers are an important connection between terrestrial ecosystems and the ocean, and are hotspots of organic carbon transformation. Riverine dissolved organic matter (DOM) concentration and composition are sensitive to both regional and global change processes. The comparative studies on riverine DOM among different rivers, especially when it comes to seasonal changes, were critical to better constrain the river carbon cycle. In this study, we analyzed organic carbon concentration, DOM sources and composition from the estuary locations of 48 Chinese rivers using the stable isotopic analysis and Fourier transform ion cyclotron resonance mass spectrometry. Both dissolved organic carbon (DOC) and particulate organic carbon (POC) concentrations, displayed significantly higher mean values in autumn than in summer (p < 0.05). Most of the DOM in these rivers entering marginal seas in four seasons came from the terrigenous sources (60.6%-68.4%), followed by the autochthonous matters (18.6%-25.6%), and the contribution of the anthropogenic sewage ranged from 7.6% to 13.7%. The proportion contributions of terrigenous and anthropogenic sources to DOM were similar in all seasons, but the contribution of autochthonous inputs showed the highest value in autumn than other seasons (p > 0.05). Although the highly unsaturated compounds are the most abundant molecular formulas detected in all riverine DOM, the molecular composition of riverine DOM shows a large difference among seven river basins. The human activity disturbance may be one of the major reasons for DOM composition differences. The contribution of sewage discharge on Chinese riverine DOM flux could be roughly estimated as 16.11–17.74 Gg C /yr. This work seeks to provide a foundation for ongoing research of regional and global riverine carbon cycling, and put forward the method for evaluating the potential impact of human disturbances on riverine organic carbon components in Chinese rivers. [ABSTRACT FROM AUTHOR]