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Abstract
As a vital constituent of water’s optical properties, the absorption coefficients influence the distribution of underwater light field, consequently impacting the structures and functional patterns of riverine ecosystems. In this study, the light absorption of non-algal particulates (ad(λ), m−1), phytoplankton (aph(λ), m−1) and CDOM (ag(λ), m−1) of 380 water samples collected from 133 rivers in eight external river basins across China from 2013 to 2023 were examined to determine the optical absorption characteristics. Results showed significant differences in ad(λ), aph(λ) and ag(λ) across different basins. ① The water bodies of eight basins can be categorized into 5 dominant types of absorption coefficients. ② In eastern China, ag(440) exhibited a northeast-high and southwest-low spatial distribution pattern. The Songliao River Basin had the highest ag(440) than other basins. The higher slope S of ag(λ) in rivers compared to lakes and reservoirs confirm river water primarily derive CDOM from external sources, distinguishing them from lakes and reservoirs. ③ The Huaihe and Haihe River Basins had higher ad(440) and aph(440) values, primarily due to lower terrain and human activities, leading to the accumulation of suspended particles and nutrients. And soil erosion from the Loess Plateau caused significant differences in ad(440) between the upper and middle reaches of the Yellow River Basin. These findings hold significant implications for understanding the optical characteristics of rivers in China.
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Non-algal particulates, phytoplankton, and colored dissolved organic matter (CDOM) are three essential components of water color within river ecosystems [1,2], their light absorption coefficients(represented as ad(λ), aph(λ) and ag(λ), respectively; Fig. 1, Table 1) are crucial optical properties of water, influencing the distribution of underwater light field [3,4]. The distribution of underwater light field in river water impacts the distribution and composition of aquatic biota communities, consequently affecting the overall functional patterns of river ecosystems [5]. Serving as a linkage between land and ocean, river ecosystems play a pivotal role in the material cycling of the biosphere and in socio-economic development [6–8]. Therefore, exploring the characterization of absorption coefficients related to the constituents of water color in various riverine water bodies is critically significant in comprehending the varied conditions and associated aspects of river ecosystems [9,10].
Fig. 1. Components of water color within river ecosystems. The absorption curve shapes of non-algal particulates, phytoplankton and CDOM are shown on the right side.
Table 1. A list of abbreviations used in the article
The light absorption coefficient of water refers to the rate at which water absorbs light along a unit path length. It offers an intuitive reflection of the composition and concentration of various optical components within water [11,12]. The distinctive absorption coefficient characteristics of different substances indirectly characterize the nature and concentration of water color components. And the absorption coefficient value at 440nm is often utilized as an indication of the concentration of different substances [13–16].Light absorption coefficient of particulates in water can be partitioned into the absorption coefficients of non-algal particulates and phytoplankton. The absorption by non-algal particulates is induced by debris such as detritus and suspended sediments in the water, whereas phytoplankton's absorption is primarily driven by diverse algal pigments, collectively forming its absorption spectrum [17,18]. CDOM mainly consists of substances like fulvic acids, humic substances, and aromatic hydrocarbon polymers [19]. It infiltrates water bodies through both external and internal routes, with external sources encompassing the breakdown of terrestrial vegetation and soil leaching, while internal sources predominantly originate from organic debris generated by phytoplankton mortality. The slope (S value) of the CDOM absorption coefficient [20–22] and the relative molecular weight parameter (M value) [23,24] are widely utilized for semi-quantitative characterization of the origin and composition of CDOM.
Currently, analyses of water absorption coefficients in inland water mainly focus on lakes and reservoirs. Owing to the influence of CDOM absorption characteristics on aquatic bacterial communities [25], global carbon cycling [26,27], and global climate warming [28], this aspect has garnered significant attention [29–31]. In the early stages, the analysis of absorption coefficients in lakes was centered on the absorption coefficients investigation of individual lake [32,33]. As the demand for extensive monitoring emerged, the study of absorption coefficient characteristics shifted from single lake to lake groups, the analysis and application efforts moving toward broader spatial scales [13,34,35]. Compared to lakes, rivers have a broader and more scattered distribution, often within rugged geographical environments. The study of river water bodies imposes greater demands in terms of artificial costs, water sample collection, and analysis due to their dispersed nature and challenging terrain. Therefore, researches on the water absorption coefficients in rivers have been relatively scarce. It mainly concentrates on the analysis of the three absorption coefficients for individual rivers [36–39] or the comparison of a single absorption coefficient across different rivers, with a primary focus on CDOM absorption coefficient [40–44]. Existing researches have been confined to grasping the absorption coefficient characteristics of rivers either at the scale of individual rivers or through the analysis of a single absorption coefficient, without capturing macroscopic differences in the characteristics of the three absorption coefficients across a broad range of riverine water bodies and lacking regional summarization.
River basins serve as fundamental units for studying and managing rivers. Analyzing the differences in absorption coefficients of river water bodies across various basins in China and subsequently understanding the optical variations is an urgent task. As the world's largest developing country, China boasts one of the highest numbers of rivers and well-developed water facilities globally [45]. Grasping the optical distinctions among river water bodies in different basins lays the foundation for large-scale ecological restoration and protection efforts in China's river basins. It is also vital for establishing emerging technological systems like optical remote sensing for river water bodies.
Addressing the limitations of existing researches and the pressing research demands, this study aims to macroscopically determine the absorption coefficient characteristics of typical river water bodies across different basins in China. Starting from the absorption coefficients of representative rivers, this research aims to achieve the following objectives: (1) Identify the dominant classification of absorption coefficients in river water bodies based on the contributions of the three major water color constituents’ absorption coefficients. (2) Analyze the wide-scale spatial variations in absorption coefficients across different river basins. (3) Investigate the sources and molecular compositions of CDOM in river water bodies across different basins.
2. Materials and methods
2.1 Eight major river basins in China and field samples
In China (73°33′E-135°05′E, 3°51′N-53°33′N), there are 2,221 rivers with basin areas exceeding 1,000 square kilometers. The spatial configurations of these rivers predominantly encompass main streams, tributaries, and river-type lakes or reservoirs [46,47]. Rivers in China are categorized as either external rivers which flow into oceans, or inland rivers which flow into enclosed lakes or disappear into deserts. Divided according to watershed regions, Chinese rivers can be categorized into nine major basins. Among the nine basins, one is an inland river basin, while the remaining eight are external river basins (Fig. 2). This study focuses on typical river water bodies within eight external river basins, including the Songliao River Basin, Haihe River Basin, Huaihe River Basin, the Yangtze River Basin, Southeast Rivers Basin, the Yellow River Basin, Southwest Rivers Basin, and Pearl River Basin.
Water samples were collected from eight external river basins in China to characterize the absorption coefficient characteristics of river water bodies within various basins. With a wide-ranging coverage across different basins, the samples possessed typicity and encompass various river ecosystems, including rivers and river-types lakes or reservoirs. In total, 380 water samples were collected from 133 rivers between 2013 and 2023 (Fig. 2). For each sampling point, geographical coordinates (latitude, longitude) and elevation were recorded in situ with a GPS receiver. All water samples were collected in 1.5L acid-cleaned plastic bottles, held on ice packs, and transported to the laboratory as soon as possible. In the laboratory, samples were stored at 4 °C in a refrigerator, and analyzed within 2 days.
The optical characteristics of water vary with seasons and spaces. Spatial difference is the focus of this study. In order to reduce the influence of seasonal factors, only autumn data were selected for comparison in this study, so that water samples in different regions could be under the same standard as far as possible, and the influence of seasonal factors could be reduced as much as possible.
2.2 Spectral measurement of absorption coefficient
After filtering the water samples through 0.7µm Whatman GF/F filters, the total particulate absorption coefficient (ap(λ)) and non-algal particulate absorption coefficient (ad(λ)) were measured using the quantitative filter technique (QFT) [48]. Measurements were conducted within the range of 400 to 750 nm at 1 nm intervals [49]. Additionally, the absorption coefficient of phytoplankton particles (aph(λ)) was calculated as the difference between ap(λ) and ad(λ).
The water samples in the laboratory were filtered through a 0.22 mm Millipore membrane cellulose filter. CDOM absorption spectra were determined using a Shimadzu UV-2600PC UV-Vis spectrophotometer, fitted with a 1cm quartz cuvette, in the spectral region between 240 and 800 nm at 1 nm intervals [50,51].
To ensure quality in the measurement of absorbance coefficients, parallel measurements were conducted at all sampling points. The relative deviations of parallel sample determinations were all within ±20%, and the final results were based on the mean value of the parallel samples.
2.3 Calculate S and M of CDOM absorption coefficient
The absorption coefficient of CDOM (ag(λ)) exhibits exponential decrease from ultraviolet to visible light range. The spectral absorbance can be modeled with Eq. (2). The slope (S value) of ag(λ) is calculated through nonlinear fitting using the least squares method [22,50]:
Here λ0 is a reference wavelength of 440 nm and S is the spectral slope.
The molecular weight parameter (M value) is the ratio between ag(250) and ag(365).
2.4 Analysis dominant type of absorption coefficient
Determine the dominant classification result of absorption coefficients using the classification method established by the International Ocean Colour Coordinating Group (IOCCG; Fig. 3) [37,52].
Fig. 3. The dominant categorizations of the absorption coefficient. Characteristic values at 440 nm are summed to determine the total absorption coefficient. The contribution ratios of individual components are obtained by dividing their characteristic values by the total absorption coefficient. Dominant categorizations are divided using the boundaries of 1/6 and 2/3 contribution ratios.
Taking the contribution rate of aph(λ) as an example, the calculation formula is as Eq. (4):
Here λ0 is a reference wavelength of 440 nm and aph (λ), ad(λ) and ag(λ) are the phytoplankton absorption coefficient, the non-algal particulate absorption coefficient and the absorption coefficient of CDOM, respectively.
Categorized based on the dominant absorption factors, the water bodies’ dominant absorption coefficient classification results can be divided into seven classes: single-factor dominance (ad, aph and ag), dual-factor dominance (ad – ag, ad – aph and aph – ag), and triple-factor dominance (ad – aph – ag) (Fig. 3). Further subcategories within each dominance type are established by considering the variations in contribution ratios of the three absorption coefficients.
2.5 Other statistical analyses and evaluation
Statistical analyses, including mean values, standard error, non-linear regressions, and Analysis of Variance(ANOVA) were performed using SPSS 16.0 software package (Statistical Program for Social Sciences, Chicago, IL).Difference is considered statistically significant when p < 0.05. Spatial mapping of sampling sites was conducted using ArcGIS 10.7 (Environmental Systems Research Institute, Redlands, CA). Data visualization was conducted using Origin 2019b software (MicroCal Software, Inc., Northampton, MA).
3. Results
3.1 Classification of absorption coefficient dominant type in basin
By classifying the dominant absorption coefficient categorization in each basin, it is evident that water bodies in Chinese basins are primarily dominated by ad, ad - ag, ad - aph, or ad - aph - ag. Water dominated by aph, ag, or aph - ag are relatively less (Fig. 4(a)-(h)). It is evident that, in inland, dissolved organic matter (DOM) and non-algal particles (NAP) play a more important role in affecting the color of water, as well as its biogeochemistry, sediment transport, and primary productivity [53,54].
Fig. 4. Classification results of dominant absorption coefficient and contribution ratios of each absorption coefficient for water bodies in different basins: (a)-(h) depict the dominant classification results for the eight basins, and (i) displays the contribution ratios of the three absorption coefficients for water bodies in different basins, with connecting lines representing the mean contribution ratios.
According to the analyses and the dominant categorizations of absorption coefficient across the basins, China's external river basins can be divided into five water types (Four dominant categorizations of absorption coefficients are involved).
Type I: In the Songliao River Basin, the water bodies are mainly dominated by ad-ag, with sampling points concentrated at the lower end of the dominant type distribution plot (Fig. 4(a)). The contribution of ag is higher than that of ad, and significantly higher than that of aph, the contribution of aph to the water bodies is extremely low (Fig. 4(i)).
Type II: In the Haihe River Basin, the water bodies are primarily dominated by ad - aph - ag, with a higher contribution ratio from aph, and lower contributions from ad and ag (Fig. 4(i)). The sampling points are concentrated in the middle and upper-right of the dominant type distribution plot (Fig. 4(b)).
Type III: In the Huaihe River Basin, water bodies are mainly co-dominated by ad -aph, with sampling points concentrated in the upper-right area of the plot (Fig. 4(c)). Water bodies in this type have a relatively higher contribution from aph and the lowest contribution from ag (Fig. 4(i)).
Type IV: In the Yangtze River Basin, Southeast Rivers, Yellow River Basin, and Pearl River Basin, the water bodies are primarily co-dominated by ad - aph - ag and single ad dominance. Among these, ad contributes the highest, followed by ag, and aph with the lowest contribution ratio. From the corners formed by the mean line segments in Fig. 4(i), it is apparent that the contribution of ad is relatively stable across different basins, while the contributions of aph and ag vary. The sampling points for this category are concentrated in the lower-right area of the dominant type distribution plot (Fig. 4(d)–(f) and Fig. 4(h)).
Type V: For the southwest river basin, there are few water sample points. According to the current law of sample points, the water bodies are primarily influenced by ad - ag dominance (Fig. 4(g)). However, different from Type I, the contribution rate of ad in this type was higher than that of ag (Fig. 4(i)). Due to the limited number of sampling points in this basin, based on the current pattern, it is tentatively categorized as Type V.
3.2 Absorption coefficient curves analysis for dominant types
Analyzing the absorption coefficient curves of the five dominant types proposed in Section 3.1, further investigation was conducted to explore the variations in absorption among different types.
It is observed that the non-algal particulate absorption coefficient for all five types exhibits a consistent negative exponential decrease as the wavelength increases, indicating a stable absorption characteristic (Fig. 5(a)-(e)). At about 670 nm, the absorption curves of water bodies from the Haihe River Basin and the Huaihe River Basin display subtle shoulder-like features (Fig. 5(b)-(c)), attributed to incomplete pigment bleaching. The divergence in the absorption coefficient of non-algal particulate matter among different water body types primarily manifests as variations in the magnitude of absorption coefficient values. It is evident that before 600 nm, significant differences exist in the mean absorption coefficients across regions, with Type III water bodies exhibiting notably elevated non-algal particulate matter absorption curves, while Type V water bodies exhibit the lowest absorption curve values. After 600 nm, the differences in the absorption coefficient values of non-algal particulate matter diminish progressively as wavelengths increase.
Fig. 5. The mean absorption coefficient curves for the five water types are presented, with solid lines indicating the mean values at each wavelength and shaded bands representing the standard error range. Each row represents an absorption coefficient, which is ad, aph and ag from top to bottom, and each column represents different optical types of water bodies.
The absorption coefficient of phytoplankton is influenced by the pigment composition in the water. In Type II (Fig. 5(g)), Type III (Fig. 5(h)), and the Yangtze River Basin and Pearl River Basin water bodies in Type IV (Fig. 5(i)), the absorption spectra of phytoplankton exhibit distinct chlorophyll absorption peaks at 440nm and 675nm, prominent carotenoid absorption shoulders at 490nm, and evident phycocyanin absorption shoulders at 620nm. This signifies the complex composition and relatively high abundance of algae in the water.
In Type I (Fig. 5(f)), as well as in the waters of the Yellow River Basin and Southeast Rivers Basin in Type IV (Fig. 5(i)), and Type V (Fig. 5(j)), a distinct absorption peak is observed at 675 nm in the absorption spectra. Notably, the absorption peaks and shoulders present at 440 nm, 490 nm, and 620 nm are nearly absent in these cases. This phenomenon is likely attributable to the dominance of non-algal particulates in the overall particle absorption spectra at these sampling points, signifying lower algal content.
Concurrently, the absence of a discernible absorption peak at 440 nm in the phytoplankton absorption coefficient spectra, coupled with the manifestation of absorption traits related to non-algal particulates within the 400-520 nm range, is likely ascribed to the prevalence of non-algal particulate absorption in the overall particle absorption coefficient. This predominance stems from the comparatively lower presence of algae. In the process of algal extraction, the simultaneous extraction of additional pigments from non-algal particulates could result in an augmented absorption characteristic of non-algal particulates, thereby exerting an influence on the observed attributes.
In Type I waters (Fig. 5(f)), as well as in the rivers of the Yellow River Basin and Southeast Rivers Basin in Type IV (Fig. 5(i)), and Type V rivers (Fig. 5(j)), phytoplankton's contribution to the total absorption coefficient is minimal. The relatively low presence of phytoplankton and their reduced content in these waters may account for the observed phenomenon where the amplification of non-algal particulate absorption affects the clarity of the 440nm absorption peak in phytoplankton absorption.
The CDOM absorption coefficient reflects the absorption characteristics of colored dissolved organic matter in water. The CDOM absorption spectra across different regions and the absorption spectra of non-algal particulates exhibit a similar negative exponential decay process, indicating a stable absorption feature (Fig. 5(k)-(o)). Before 500nm, there are notable differences in the mean absorption coefficients among the regions. After 500nm, the differences in the mean CDOM absorption coefficients among regions diminish. Notably, in Type I (Fig. 5(k)), the CDOM absorption coefficient is significantly higher than in other types. Conversely, Type III (Fig. 5(m)) and Type IV water (Fig. 5(n)) generally exhibit lower CDOM absorption coefficients. Additionally, the CDOM absorption curve for Type V water (Fig. 5(o)) indicates the lowest values.
4. Discussion
4.1 Differences and causes in absorption coefficients
The watershed's crestline, also termed as the watershed divide, encircles the river's catchment area, thus defining the river's basin. This distinct geographical demarcation between watersheds profoundly influences the exchange of water bodies across distinct basins, leading to variations in composition among river water bodies within different basins. In this study, water absorption coefficients at 440nm are analyzed as characteristic values on a basin-wise basis, aiming to elucidate the disparities in absorption coefficients among different basins and comprehend the underlying rationales for these variations.
Consequently, across different basins, there are significant variations in water absorption coefficients at 440 nm (Table 2), indicating substantial differences in the composition of substances within water bodies across distinct basins. Within a single basin, the standard errors of the three absorption coefficients are relatively small (Table 2), suggesting that differences among rivers within a basin are comparatively minor and possess similar characteristics.
Table 2. Statistical values of three absorption coefficients in water bodies across various basins. Employ the values of each absorption coefficient at 440nm as characteristic parameters. These values indirectly represent the concentrations of various water quality constituents. N denotes the sample count, while µ ± SEM represents the mean value and standard error of the corresponding parameter.
In Huaihe River Basin, the ad(440) exhibits the highest value among the eight basins, reaching 3.89 ± 0.87 m-1, which is thirteen times that of the Southwest River Basin (0.29 ± 0.03 m-1) (Table 2). Notably, there are significant disparities in ad(440) among different basins (p < 0.001). The heightened ad(440) in the Huaihe River Basin implies a greater sediment content within its river water.
This observation can be ascribed to two principal factors. Firstly, during field sampling, the significant problem of river desiccation was observed in the Huaihe River Basin, with certain rivers even exposing their riverbeds. This reduction in river water volume causes the accumulation of suspended materials within the water. Secondly, the Huaihe River Basin accommodates 1/8 of China's total population, and it is significantly influenced by anthropogenic activities. The region encounters substantial soil erosion driven by human-induced factors. Based on data from 2005, the extent of soil erosion encompassed an area of 30,800 km2 in the Huaihe River Basin, accounting for 11.62% of the total basin area. The average soil erosion was estimated to be 158 million tons, with an average erosion modulus of 784.6 t·km-2·a-1 [55–57].
The range of mean aph(440) values across various basins spans from 0.05 ± 0.01 m-1 to 2.58 ± 0.43 m-1, underscoring significant different in aph(440) among these basins (p < 0.001). In both the Huaihe River Basin and the Haihe River Basin, aph(440) not only contributes substantially to the overall absorption coefficient but also manifests higher levels when juxtaposed with other basins. Specifically, the aph(440) values for these two basins are 2.58 ± 0.43 m-1 and 1.80 ± 0.23 m-1, respectively (Table 2), indicating heightened phytoplankton concentrations in their aquatic environments.
The Huaihe River Basin and the Haihe River Basin function as pivotal regions undergoing urban and agricultural expansion [58,59], playing vital roles as breeding and cultivation hubs. These areas face agricultural runoff and associated pollution [60,61], leading to elevated nutrient levels like nitrogen and phosphorus in the waters. As a result, these waters tend to undergo eutrophication [62,63]. According to the National Surface Water Environmental Quality Report issued by the Ministry of Environmental Protection of China, the water quality of the Huaihe River Basin has been consistently categorized as lightly polluted during certain months over three consecutive years (2020-2022). Similarly, the Haihe River Basin has maintained a rating of mild water quality pollution for three successive years. With a greater prevalence of organic pollutants in rivers [63], these regions provide conducive conditions for phytoplankton growth. These phenomena clarify the reason behind the dominant presence of phytoplankton in the waters of the Huaihe River Basin and the Haihe River Basin, resulting in higher aph(440) values compared to other basins.
In the eastern region of China (east of the Heihe-Tengchong Line), there is an overall spatial distribution pattern of ag(440) that is higher in the northeast and lower in the southwest (Fig. 6). In the Songhua River Basin, where ag(λ) significantly influences water absorption coefficients, ag(440) is measured at 3.06 ± 0.40 m-1, considerably surpassing values in other basins and displaying a dramatic contrast (Table 2). It is roughly 3 to 11 times higher than the values observed in other basins. Significantly, a substantial different in ag(440) exists among basins (p < 0.001). CDOM in aquatic systems primarily arises from both internal sources, stemming from the decay of phytoplankton, and external sources, including terrestrial organic matter. When aph(440) values are extremely low while ag(440) values remain elevated, as observed in the Songhua River Basin, it suggests that the CDOM in this region primarily originates from external terrestrial organic matter.
Fig. 6. The spatial distribution map of ag(440) values at different water sampling points, with the Hu Huanyong Line extending from Heihe City (47°42′N-51°03′N, 124°45′E-129°18′E) to Tengchong City (24°38′N-25°52′N, 98°05′E-98°46′E).
Similarly, it was also noted that the ag(440) values in reservoirs within the Songhua River Basin were notably elevated compared to those in other regions [64]. This is attributed to the fact that the Songhua River Basin is situated in Northeastern China, known as one of the world's four major black soil regions, characterized by its rich humic content in the soil. Meanwhile, the northeastern region experiences severe soil erosion, primarily through water erosion. A significant quantity of humic substances is transported into river channels through soil erosion and surface runoff processes [65–67]. Furthermore, the residence time of water is a crucial factor influencing the biogeochemical cycling of dissolved organic matter in aquatic systems [37,68]. In the case of the Songhua River Basin, water bodies experience an extended ice-covered period lasting up to five months, resulting in longer water residence times. Consequently, the impact of photobleaching on CDOM is relatively minimal in this region [69].
In the Yellow River Basin and the Southwest Rivers Basin, the ag(440) values are lower, measuring 0.25 ± 0.02 m-1 and 0.26 ± 0.01 m-1, respectively (Table 2). This phenomenon may attributed to their high photosensitivity, as CDOM is more prone to decomposition in regions with abundant sunlight [22].
4.2 Sources and components analysis of CDOM
To further investigate the spatial variability of CDOM, employ the slope S and relative molecular weight M as semi-quantitative indicators to characterize the source and composition of CDOM [21,24]. The commonly used slope range from 280nm to 500nm was selected, and the S280-500 was fitted using the least squares method. There were significant differences in S280-500 among the various basins (p < 0.0001), with mean values ranging from 0.0060 ± 0.0001 nm−1 to 0.0215 ± 0.0009 nm−1 (Table 3). This indicates variations in the relative proportions of fulvic acids and humic substances in CDOM among different regions [21]. Compared to rivers in other basins, the Songhua River Basin exhibits a higher S280-500, indicating a higher proportion of fulvic acids in CDOM. In contrast, in basins like the Pearl River and the Yangtze River, the S280-500 values are lower, suggesting a higher proportion of humic acids in CDOM.
Table 3. The statistics values of S and M in different basins. N represents the sample size, while µ ± SEM represents the mean value and standard error of the corresponding parameter.
Among the typical rivers within the basins, the Tangwang River (128°54'E, 47°46'N) exhibits the highest average S280-500 measuring 0.0290 ± 0.0005 nm−1. The Songhua River and rivers in Changchun City (125°19'E, 43°49'N) follow, with S280-500 of 0.0200 ± 0.0014 nm−1 and 0.0198 ± 0.0005 nm−1, respectively (Fig. 7(a)). The S280-500 in Dongting Lake (113°10'E, 29°7'N), Shanmei Reservoir (118°25′E, 25°12′N), Longyangxia Reservoir (100°55'E, 36°07'N), and Manwan Reservoir (100°26′E, 24°38′N) are notably lower than in other rivers. This is attributed to the significant differences in CDOM sources between lakes and rivers. Riverine CDOM primarily originates from external sources, whereas lakes and reservoirs are predominantly influenced by internal CDOM sources [70]. However, in this study, the S280-500 value in Guanting Reservoir (115°49′E, 40°24′N) is relatively high among lakes and reservoirs (Fig. 7(a)). The elevated S280-500 value in Guanting Reservoir may be due to the non-compliance of water quality at the reservoir's inlet which meet Class V water standards, indicating a higher level of eutrophication and significant organic pollution, mainly comprising nitrogen and phosphorus [71].
Fig. 7. The S and M of CDOM absorption coefficient in different sampling rivers (or lakes and reservoirs), the brown circles in the scatterplot of S values indicate high-value and low-value points.
CDOM relative molecular weight (M value) reflects the proportion of humic acid and fulvic acid in CDOM. As molecular size increased, M decreased because of stronger light absorption by high-molecular-weight (HMW) CDOM at longer wavelengths. The larger the M, the lower the relative content of humic acid in CDOM, and the higher the relative content of fulvic acid, resulting in a smaller relative molecular weight [23,24]. The mean M in the Yellow River Basin and Haihe River Basin are relatively high, measuring 8.2530 ± 0.2850 and 7.4920 ± 0.2030, respectively. And the mean M in the Southeast Rivers Basin is lower at 5.3740 ± 0.1430 (Table 3). In Chinese rivers, the M range between 5.5 and 8 (Fig. 7(b)), whereas in American rivers, M values typically fall between 5 and 6.5 [43]. This indicates that the mean M of certain Chinese rivers exceed those of American rivers, suggesting that the rivers in Chinese exhibit smaller relative molecular weights and relatively lower degrees of humification.
4.3 Absorption coefficient characterization of the Yellow River’s different sections
The Loess Plateau (100°52'E-114°33'E, 33°41'N-41°16'N) is globally recognized as one of the regions most severely affected by soil erosion. As the Yellow River flows through the Loess Plateau, it transports a substantial volume of sediment, ranking it as the world's second-largest river in terms of sediment transport [6,72]. The Yellow River exhibits significant variations in its characteristics along its course. In the upper reaches of the Yellow River, there is a significant drop in elevation, and the water remains clear. However, as the river traverses the Loess Plateau region in its middle reaches, it carries a substantial volume of sediment into the river. In fact, it is noteworthy that close to 90% of the sediment entering the Yellow River originates from the middle reaches of the river basin [8,73].
The Longyangxia Reservoir (100°55'E, 36°07'N) is a significant reservoir in the upper reaches of the Yellow River, where the water is primarily dominated by triple-factor (ad - aph - ag) (Fig. 8(a)). On the other hand, the Weihe River (109°19'E, 34°32'N) is located in the middle reaches of the Yellow River and is one of its largest tributaries, with water mainly dominated by ad (Fig. 8(a)). Particularly noteworthy is the substantial difference in ad(440) between the Weihe River and the Longyangxia Reservoir, with the former being approximately 21 times higher than the latter. Similarly, ag(440) in the Weihe River is roughly twice as high as that in the Longyangxia Reservoir. In contrast, the variation in aph(440) is relatively minor (Table 4).
Fig. 8. (a) is the classification results of dominant absorption coefficient in Longyangxia Reservoir and Weihe River. (b)-(d): The mean absorption coefficient curves in Longyangxia Reservoir and Weihe River.
Table 4. Water absorption coefficient eigenvalues in Longyangxia Reservoir and Weihe River
The disparity in ad(440) between the Longyangxia Reservoir and the Weihe River signifies a substantial difference in sediment content within these water bodies. This phenomenon can be attributed not only to the significant sediment load transported by the Yellow River as it traverses the Loess Plateau but also to the comparatively tranquil nature of reservoir water in contrast to rivers. Reservoirs tend to facilitate sediment settling due to their calmer conditions, resulting in a spatial distribution of suspended material concentration, with the highest levels typically found in river sections, followed by transitional zones, and finally, reservoir areas [74]. In certain instances, the substantial sediment transport from inflowing rivers can lead to severe sediment deposition within the reservoir, further accentuating the pronounced difference in sediment content between the Longyangxia Reservoir and the Weihe River.
The difference in ag(440) between the Longyangxia Reservoir and the Weihe River can be primarily attributed to differences in land use patterns. The Longyangxia Reservoir in the upper reaches of the Yellow River is predominantly surrounded by ecologically rich areas characterized by forests and grasslands [75]. Human influence in this region is minimal, as indicated in Section 4.2, which also demonstrates lower S values for CDOM in the Longyangxia Reservoir water (Fig. 7(a)), signifying a predominant contribution of CDOM from internal sources. In contrast, the Weihe River, located in the middle reaches of the Yellow River, flows through several large and medium-sized cities in northwestern China, including Xi'an and Baoji. The land-use intensity in this region is considerably higher, and it experiences a more pronounced influence from human activities.
5. Conclusion
Although 380 sampling points are indeed insufficient for the water bodies of Chinese rivers, they cannot represent the whole picture, these data are still valuable and meaningful, which are the preliminary exploration of the absorption coefficient characteristics of the water bodies of Chinese rivers. Based on these data, the following rules are summarized.
Conclusion 1: Water bodies in eight external river basins in China have been categorized into five types based on dominant patterns of absorption coefficients. Type I: Water bodies in the Songhua River basin are primarily influenced by ad and ag, with ag contributing the most. Type II: In the Haihe River basin, dominated by all three factors, but aph has a higher contribution than ad and ag. Type III: Water bodies in the Huaihe River basin are mainly influenced by ad and aph, with aph contributing relatively more and ag contributing the least. Type IV: In the Yangtze River basin, Southeast rivers, Yellow River basin, and Pearl River basin, dominated by all three factors, with ad having the highest contribution. Type V: Water bodies in the Southwest rivers basin are primarily influenced by ad and ag, but unlike Type I, ad has a higher contribution than ag.
Conclusion 2: The spatial variability in ag(440) is most pronounced among different river basins. In eastern China, ag(440) exhibits a spatial distribution pattern characterized by higher values in the northeast and lower values in the southwest. In the Songhua River Basin, abundant soil-derived humic substances and leaching processes significantly elevate CDOM levels compared to other basins. In rivers, CDOM mainly originates from external sources, supported by significantly higher S values of CDOM absorption coefficients in river water compared to reservoirs, as demonstrated in this study.
Conclusion 3: There are significant spatial variations in ad(440) and aph(440) among different river basins. Due to the impact of human activities and the relatively low topography, suspended particles continuously accumulate, resulting in higher ad(440) values in the Huaihe River basin. The rivers in the Huaihe River basin and Haihe River basin tend to become eutrophic, accumulating nutrients, which is reflected in significantly higher aph(440) values compared to other basins. The substantial difference in ad(440) between the Longyangxia Reservoir in the upper reaches and the Weihe River in the middle reaches of the Yellow River basin can be attributed to significant variations in sediment content within these water bodies. This discrepancy is primarily a result of soil erosion occurring in the Loess Plateau region.
Funding
National Key Research and Development Program of China (2021YFB3901101).
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are original and not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. J. R. Ramus and J. T. O. Kirk, “Light and photosynthesis in aquatic ecosystems,” J. Appl. Ecol. 22(3), 1010 (1985). [CrossRef]
2. A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters),” J. Geophys. Res. 93(C9), 10749–10768 (1988). [CrossRef]
3. A. Morel and L. Prieur, “Analysis of variations in ocean color1,” Limnol. Oceanogr. 22(4), 709–722 (1977). [CrossRef]
4. R. M. Pope and E. S. Fry, “Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements,” Appl. Opt. 36(33), 8710–8723 (1997). [CrossRef]
5. B. Sulzberger and E. Durisch-Kaiser, “Chemical characterization of dissolved organic matter (DOM): A prerequisite for understanding UV-induced changes of DOM absorption properties and bioavailability,” Aquat. Sci. 71(2), 104–126 (2009). [CrossRef]
6. J. D. Milliman and R. H. Meade, “World-wide delivery of river sediment to the oceans,” J. Geol. (Chicago, IL, U. S.) 91(1), 1–21 (1983). [CrossRef]
7. M. Meybeck and C. Vorosmarty, “Fluvial filtering of land-to-ocean fluxes: from natural Holocene variations to Anthropocene,” C. R. Geosci. 337(1-2), 107–123 (2004). [CrossRef]
8. H. Wang, Z. Yang, Y. Saito, et al., “Stepwise decreases of the Huanghe (Yellow River) sediment load (1950-2005): Impacts of climate change and human activities,” Global and Planetary Change 57(3-4), 331–354 (2007). [CrossRef]
9. J. W. Wei, Z. P. Lee, S. Shang, et al., “Semianalytical derivation of phytoplankton, CDOM, and detritus absorption coefficients from the Landsat 8/OLI reflectance in coastal waters,” Journal of Geophysical Research-Oceans 124(6), 3682–3699 (2019). [CrossRef]
10. C. B. Mouw, S. Greb, D. Aurin, et al., “Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions,” Remote Sensing of Environment 160, 15–30 (2015). [CrossRef]
11. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters, Light and Water: Radiative Transfer in Natural Waters (Academic Press, San Diego, 1994).
12. J. R. V. Zaneveld, A. Barnard, E. Boss, et al., “Theoretical derivation of the depth average of remotely sensed optical parameters,” Opt. Express 13(22), 9052 (2005). [CrossRef]
13. K. Shi, Y. Zhang, K. Song, et al., “A semi-analytical approach for remote sensing of trophic state in inland waters: Bio-optical mechanism and application,” Remote Sensing of Environment 232, 111349 (2019). [CrossRef]
14. S. Shang, Z. Lee, G. Lin, et al., “Progressive scheme for blending empirical ocean color retrievals of absorption coefficient and chlorophyll concentration from open oceans to highly turbid waters,” Appl. Opt. 58(13), 3359–3369 (2019). [CrossRef]
15. M. Twardowski, M. R. Lewis, A. H. Barnard, et al., “In-water instrumentation and platforms forocean color remote sensing applications,” Remote Sensing of Coastal Aquatic Environments 7, 69–100 (2007). [CrossRef]
16. E. Organelli, C. Nuccio, L. Lazzara, et al., “On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions,” Appl. Opt. 56(14), 3952–3968 (2017). [CrossRef]
17. N. Hoepffner and S. Sathyendranath, “Determination of the major groups of phytoplankton pigments from the absorption spectra of total particulate matter,” J. Geophys. Res. 98(C12), 22789–22803 (1993). [CrossRef]
18. R. Bidigare, M. E. Ondrusek, J. H Morrow, et al., “In-vivo absorption properties of algal pigments,” Proc. SPIE. 1302, 290 (1990). [CrossRef]
19. R. J. DaviesColley and W. N. Vant, “Absorption of light by yellow substance in freshwater lakes,” Limnol. Oceanogr. 32(2), 416–425 (1987). [CrossRef]
20. R. Ma, R. Ma, J. Tang, et al., “Absorption and scattering properties of water body in Taihu Lake, China: absorption,” International Journal of Remote Sensing 27(19), 4277–4304 (2006). [CrossRef]
21. K. L. Carder, R. G. Steward, G. R. Harvey, et al., “Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll,” Limnol. Oceanogr. 34(1), 68–81 (1989). [CrossRef]
22. J. R. Helms, A. Stubbins, J. D. Ritchie, et al., “Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter,” Limnol. Oceanogr. 53(3), 955–969 (2008). [CrossRef]
23. H. Dehaan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993). [CrossRef]
24. J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta 337(2), 133–149 (1997). [CrossRef]
25. L. Zhang, W. Fang, X. Li, et al., “Strong linkages between dissolved organic matter and the aquatic bacterial community in an urban river,” Water Res. 184, 116089 (2020). [CrossRef]
26. J. J. Cole, Y. Prairie, N. Caraco, et al., “Plumbing the global carbon cycle: Integrating inland waters into the terrestrial carbon budget,” Ecosystems 10(1), 172–185 (2007). [CrossRef]
27. M. Gonnelli, S. Vestri, C. Santinelli, et al., “Chromophoric dissolved organic matter and microbial enzymatic activity. A biophysical approach to understand the marine carbon cycle,” Biophys. Chem. 182, 79–85 (2013). [CrossRef]
28. Y. L. Zhang, L. Zhou, Y. Zhou, et al., “Chromophoric dissolved organic matter in inland waters: Present knowledge and future challenges,” Sci. Total Environ. 759, 143550 (2021). [CrossRef]
29. P. Kowalczuk, J. Stoń-Egiert, W. J. Cooper, et al., “Characterization of chromophoric dissolved organic matter (CDOM) in the Baltic Sea by excitation emission matrix fluorescence spectroscopy,” Mar. Chem. 96(3-4), 273–292 (2005). [CrossRef]
30. E. Rochelle-Newall, F. D. Hulot, J. L. Janeau, et al., “CDOM fluorescence as a proxy of DOC concentration in natural waters: a comparison of four contrasting tropical systems,” Environ. Monit. Assess. 186(1), 589–596 (2014). [CrossRef]
31. E. L. Hestir, V. Brando, G. Campbell, et al., “The relationship between dissolved organic matter absorption and dissolved organic carbon in reservoirs along a temperate to tropical gradient,” Remote Sens. Environ. 156, 395–402 (2015). [CrossRef]
32. C. E. Binding, J. H. Jerome, R. P. Bukata, et al., “Spectral absorption properties of dissolved and particulate matter in Lake Erie,” Remote Sens. Environ. 112(4), 1702–1711 (2008). [CrossRef]
33. D. Y. Sun, Y. Li, Q. Wang, et al., “Parameterization of water component absorption in an inland eutrophic lake and its seasonal variability: a case study in Lake Taihu,” International Journal of Remote Sensing 30(13), 3549–3571 (2009). [CrossRef]
34. K. S. Song, Y. Shang, Z. Wen, et al., “Characterization of CDOM in saline and freshwater lakes across China using spectroscopic analysis,” Water Res. 150, 403–417 (2019). [CrossRef]
35. Y. Zhang, Y. Zhou, K. Shi, et al., “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018). [CrossRef]
36. T. T. Shao, T. Wang, X. Liang, et al., “Seasonal dynamics of light absorption by suspended particulate matter and CDOM in highly turbid inland rivers on the Loess Plateau, China,” River Research and Applications 35(7), 905–917 (2019). [CrossRef]
37. Z. D. Wen, K. S. Song, Y. Zhao, et al., “Influence of environmental factors on spectral characteristics of chromophoric dissolved organic matter (CDOM) in Inner Mongolia Plateau, China,” Hydrol. Earth Syst. Sci. 20(2), 787–801 (2016). [CrossRef]
38. Z. Y. Liao, Y. Wang, K. Xie, et al., “Photochemistry of dissolved organic matter in water from the Pearl river (China): Seasonal patterns and predictive modelling,” Water Res. 208, 117875 (2022). [CrossRef]
39. E. J. D’Sa and R. L. Miller, “Bio-optical properties in waters influenced by the Mississippi River during low flow conditions,” Remote Sens. Environ. 84(4), 538–549 (2003). [CrossRef]
40. S. J. Li, J. Zhang, E. Guo, et al., “Dynamics and ecological risk assessment of chromophoric dissolved organic matter in the Yinma River Watershed: Rivers, reservoirs, and urban waters,” Environ. Res. 158, 245–254 (2017). [CrossRef]
41. T. Jiang, D. Wang, S. Wei, et al., “Influences of the alternation of wet-dry periods on the variability of chromophoric dissolved organic matter in the water level fluctuation zone of the Three Gorges Reservoir area, China,” Sci. Total Environ. 636, 249–259 (2018). [CrossRef]
42. Y. Wang, D. Zhang, Z. Shen, et al., “Characterization and spacial distribution variability of chromophoric dissolved organic matter (CDOM) in the Yangtze Estuary,” Chemosphere 95, 353–362 (2014). [CrossRef]
43. R. G. M. Spencer, K. D. Butler, G. R. Aiken, et al., “Dissolved organic carbon and chromophoric dissolved organic matter properties of rivers in the USA,” J. Geophys. Res. 117, (2012).
44. L. Arvola, M. Leppäranta, C. Äijälä, et al., “CDOM variations in Finnish lakes and rivers between 1913 and 2014,” Sci. Total Environ. 601-602, 1638–1648 (2017). [CrossRef]
45. X. X. Wang, X. Xiao, Y. Qin, et al., “Improved maps of surface water bodies, large dams, reservoirs, and lakes in China,” Earth Syst. Sci. Data 14(8), 3757–3771 (2022). [CrossRef]
46. C. Nilsson, C. A. Reidy, M. Dynesius, et al., “Fragmentation and flow regulation of the world's large river systems,” Science 308(5720), 405–408 (2005). [CrossRef]
47. N. M. Hayes, B. R. Deemer, J. R. Corman, et al., “Key differences between lakes and reservoirs modify climate signals: A case for a new conceptual model,” Limnol. Oceanogr. Lett. 2(2), 47–62 (2017). [CrossRef]
48. B. G. MitchellD. A. Kiefer, “Determination of absorption and fluorescence excitation spectra for phytoplankton,” in Marine Phytoplankton and Productivity: Proceedings of the 5th conference of the European Society for Comparative Physiology and Biochemistry — Taormina, Sicily, Italy, September 5–8, 1983, O. Holm-Hansen, L. Bolis, R. Gilles, et al., eds. (Springer Berlin Heidelberg, Berlin, Heidelberg, 1984), pp. 157–169.
49. J. S. Cleveland, “Regional models for phytoplankton absorption as a function of chlorophyll a concentration,” J. Geophys. Res. 100(C7), 13333–13344 (1995). [CrossRef]
50. A. Bricaud, A. Morel, L. Prieur, et al., “Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains1,” Limnol. Oceanogr. 26(1), 43–53 (1981). [CrossRef]
51. J. Mueller, G. S. Fargion, C. R. McClain, et al., Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Rev. 4, Vol. IV: Inherent Optical Properties: Instruments, Characterisations, Field Measurements and Data Analysis Protocols (Goddard Space Flight Space Centre, Greenbelt, MD, USA, 2003).
52. IOCCG., “IOCCG Report Number 03: Remote sensing of ocean colour in coastal, and other optically-complex, waters,” IOCCG web page3(October) 1–145 (2000).
53. C. B. Mouw, A. B. Ciochetto, B. Grunert, et al., “Expanding understanding of optical variability in Lake Superior with a 4-year dataset,” Earth Syst. Sci. Data 9(2), 497–509 (2017). [CrossRef]
54. K. A. Casey, C. S. Rousseaux, W. W. Gregg, et al., “A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications,” Earth Syst. Sci. Data 12(2), 1123–1139 (2020). [CrossRef]
55. T. Cai, M. Yu, G. Lu, et al., “Investigation into the impacts of land-use change on sediment yield characteristics in the upper Huaihe River basin, China,” Physics and Chemistry of the Earth 53-54, 1–9 (2012). [CrossRef]
56. B. Wang, “Zoning of soil and water conservation monitoring in Huaihe River Basin,” Sci Soil Water Conserv 13, 51–57 (2015).
57. W. U. Nan, “Research of land use and soil erosion in upstream massif area of Huaihe river basin based on RS and GIS,” Journal of Anhui Agricultural University 34, 589–595 (2007).
58. L. Wang, C. C. Li, Q. Ying, et al., “China's urban expansion from 1990 to 2010 determined with satellite remote sensing,” Chin. Sci. Bull. 57(22), 2802–2812 (2012). [CrossRef]
59. Y. S. Liu, L. Wang, H. Long, et al., “Spatio-temporal analysis of land-use conversion in the eastern coastal China during 1996-2005,” J. Geogr. Sci. 18(3), 274–282 (2008). [CrossRef]
60. L. Zhou, J. G. Xu, D. Q. Sun, et al., “Spatial heterogeneity and classified control of agricultural non-point source pollution in Huaihe River Basin,” Huanjing Kexue 34, 547–554 (2013).
61. J. Gao, “Evolution of temporal-spatial pattern of grain production and its driving mechanism in Huaihe River Basin during 1990-2012,” Bull. Soil Water Conserv. 36, 179 (2016).
62. X. Y. Yang, Q. Liu, G. Fu, et al., “Spatiotemporal patterns and source attribution of nitrogen load in a river basin with complex pollution sources,” Water Res. 94, 187–199 (2016). [CrossRef]
63. M. Strokal, C. Kroeze, M. Wang, et al., “The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China,” Sci. Total Environ. 562, 869–888 (2016). [CrossRef]
64. Y. X. Shang, K. Song, P. A. Jacinthe, et al., “Characterization of CDOM in reservoirs and its linkage to trophic status assessment across China using spectroscopic analysis,” J. Hydrol. (Amsterdam, Neth.) 576, 1–11 (2019). [CrossRef]
65. X. W. Duan, Y. Xie, T. Ou, et al., “Effects of soil erosion on long-term soil productivity in the black soil region of northeastern China,” Catena 87(2), 268–275 (2011). [CrossRef]
66. X. B. Liu, X. Y. Zhang, Y.X. Wang, et al., “Soil degradation: a problem threatening the sustainable development of agriculture in Northeast China,” Plant Soil Environ. 56(2), 87–97 (2010). [CrossRef]
67. B. Zhang, X. Song, Y. Zhang, et al., “Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China,” Water Res. 46(8), 2737–2748 (2012). [CrossRef]
68. J. R. Helms, J. Mao, A. Stubbins, et al., “Loss of optical and molecular indicators of terrigenous dissolved organic matter during long-term photobleaching,” Aquat. Sci. 76(3), 353–373 (2014). [CrossRef]
69. T. Brinkmann, D. Sartorius, F. H. Frimmel, et al., “Photobleaching of humic rich dissolved organic matter,” Aquat. Sci. 65(4), 415–424 (2003). [CrossRef]
70. S. Tao, “Origin, content and regional differentiation of humic acid in major rivers in eastern China,” Acta Scientiae Circumstantiae, 286-294[in Chinese] (1988).
71. D. Dai, G. Huang, K. Lei, et al., “Improvement in water pollution control alters nutrient stoichiometry of Guanting Reservoir near Beijing, North China,” Environ. Sci. Pollut. Res. 30(16), 45924–45935 (2023). [CrossRef]
72. H. J. Wang, Z. Yang, Y. Saito, et al., “Interannual and seasonal variation of the Huanghe (Yellow River) water discharge over the past 50 years: Connections to impacts from ENSO events and dams,” Global and Planetary Change 50(3-4), 212–225 (2006). [CrossRef]
73. Z. Yang, H. Wang, Y. Saito, et al., “Dam impacts on the Changjiang (Yangtze) River sediment discharge to the sea: The past 55 years and after the Three Gorges Dam,” Water Resour. Res. 42(2006).
74. P. Shi, G. W. Zhu, W. B. Yang, et al., “Spatial-temporal distribution of suspended solids and its sedimentation flux and nutrients effects in Xin'anjiang Reservoir, China,” Huanjing Kexue 41, 2137–2148 (2020). [CrossRef]
75. Y. Song, “Change characteristics and formation mechanism of the territorial spatial pattern in the Yellow River Basin from 1980 to 2018,” China Geogr. Res. 40, 1445–1463 (2021).
