Characterization of chromophoric dissolved organic matter in lakes across the Tibet-Qinghai Plateau using spectroscopic analysis

Journal Pre-proofs Research papers Characterization of chromophoric dissolved organic matter in lakes across the Tibet-Qinghai Plateau using spectroscopic analysis Kaishan Song, Sijia Li, Zhidan Wen, Lili Lyu, Yingxin Shang PII: DOI: Reference:

S0022-1694(19)30925-4 https://doi.org/10.1016/j.jhydrol.2019.124190 HYDROL 124190

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Journal of Hydrology

Received Date: Revised Date: Accepted Date:

25 June 2019 27 August 2019 27 September 2019

Please cite this article as: Song, K., Li, S., Wen, Z., Lyu, L., Shang, Y., Characterization of chromophoric dissolved organic matter in lakes across the Tibet-Qinghai Plateau using spectroscopic analysis, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124190

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Characterization of chromophoric dissolved organic matter in lakes across the Tibet-Qinghai Plateau using spectroscopic analysis Kaishan Song1§*, Sijia Li1,2§, Zhidan Wen1, Lili Lyu1, Yingxin Shang1 1

Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, China

2

School of Environmental, Northeast Normal University, Changchun, 136000, China

§

K.S and S.L contributed equally to this work; *Email: [email protected]

Abstract. 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 les 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 (φi, i=I, II, III, IV, V) and total fluorescence intensities (φT) are significantly different between brackish and freshwater lakes (p<0.05). High correlations (-0.51**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.

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Likewise, average CDOM spectroscopic indices in each basin with different land covers showed a moderate correlation between DOC and normalized humic-like φV 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. Keywords: CDOM; Tibet-Qinghai Plateau; Fluorescence; Brackish lakes; FRI

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1. Introduction Inland lakes have a direct link between the terrestrial and atmospheric CO2 pools, and they have an indirect link between the oceans via rivers. Inland lakes play a vital role in transportation, transformation and storage of carbon from terrestrially imported substances (Cole et al., 2007; Tranvik et al., 2009). Carbon flux and biogeochemical processes of lakes have a significant influence on the global carbon cycle, and they affect regional climate (Battin et al., 2009; Jiao et al., 2010; Ran et al., 2013; Carlson et al., 2011). It is therefore important to investigate biogeochemical cycling of carbon in lakes in different regions that have distinct properties (Cole et al., 2007; Song et al., 2018a, 2018b and 2019). The Tibet-Qinghai Plateau (TQP), commonly known as the ‘Third Pole’ and the ‘Asian water tower’, has an average elevation over 4500 m (Song et al., 2016). Due to the dry and thin aerosol with a low concentration of ozone in this area, there are strong Ultraviolet-B (UV-B) radiation-penetration inhibiting properties (Ren et al., 1997). Prolonged sunshine and arid environment make some terminal lakes on the TQP have a high salt content. The high salinity lakes often paralleled with accumulation of DOC and dissolved organic matter (Zhao et al., 2016; Song et al., 2018a, 2018b). The TQP is therefore of particular interest for environmental evolution and the carbon cycling. This attributed to accumulated DOC and DOM contents in arid environment are also strongly affected by intensified UV-B radiation-penetration. Particularly, these environmental influences could change photochemical degradation rate for DOC/DOM (Spencer et al. 2012; Yao et al., 2011; Song et al., 2017a). To date, CDOM optical characteristics and their effect on carbon budget contribution have been reported in plateaus and high-mountain lakes (Wen et al., 2016; Zhang et al., 2010). However, little is currently known about CDOM in waters located in the TQP. 3

In addition, land cover types within the lake catchment affect soil runoff and leaching, having a non-negligible effect on DOC/DOM inputs and dissolved solids (Wen et al., 2016). These effects may result in obvious differences in physiochemical properties between the lakes (Heinz et al., 2015; Song et al., 2013). In particular, there are less anthropogenic activities due to high elevation of the TQP. More grass and un-used land were distributed in this region. Consequently, these special environmental characteristics make the DOC/DOM different from other regions, contributing to the relatively high average carbon budget of inland waters (Hanson et al., 2014; Song et al., 2017a, 2018a;). Chromophoric DOM (CDOM, typically <0.22 μm) is a light-absorbing substance in aquatic environments. CDOM originates from the decomposition of algae by microorganisms (autochthonous) and surrounding allochthonous environment (Singh et al., 2010; Zhang et al., 2010). Chemical properties cause CDOM to absorb light and re-emit it as fluorescent DOM (FDOM) (Stedmon et al., 2003; Zhang et al., 2010). Due to the high selectivity and sensitivity of FDOM, fluorescence spectroscopy have provided detailed insights into its composition and components (Stedmon et al., 2003; Zhang et al., 2010). Three-dimensional excitation−emission matrices (EEMs) are widely used to quantify FDOM. The intensity of FDOM from single sample is given as a function of excitation and emission wavelengths (Hudson et al., 2007). Then the intensity of FDOM show a relative fluorescent concentration (Ishii et al., 2012; Zhao et al., 2016). Then we can follow and retrieve the evolved fingerprint of FDOM based on the types and locations of EEMs’ peaks (Stedmon et al., 2003). To date, EEMs is a simple and effective technique, which is commonly used to evaluate FDOM by visual inspection (Zhang et al., 2011; Zhao et al., 2017; Zhou et al., 2016). Multivariate statistical parameters and tools, i.e., spectroscopic characterization

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(specific ultraviolet absorbance and spectral slope ratio), humification index (HIX), fluorescence index (FI), parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI), have been utilized to identify bio-geochemically meaningful components of CDOM of different sources (Coble, 1996; Chen et al., 2003; Helms et al., 2008; Stedmon et al., 2003; Zhang et al., 2010; Zhao et al., 2017). Particularly, EEM-PARAFAC and EEM-FRI techniques were widely used in determination of the FDOM components. They can transfer complex EEMs into independent fluorescent components, which have been widely used in the FDOM studies of inland waters (Zhang et al., 2010; Zhao et al., 2017). EEM-FRI (a quantitative technique) can integrate the volumes beneath defined by regions of EEM largely based on supporting literature (Chen et al., 2003). This is a function of the wavelength ranges associated with different fluorescence peaks in each EEM. Then EEM-FRI covers continuous fluorescence intensity at excitation-emission wavelength of divided regions for further analysis (Chen et al., 2003). Compared to other fluorescence tools (EEM-PARAFAC), EEM-FRI could calculate the volumetric signal (i.e., ΦT,n). The EEM-FRI technique is more robust, because it encompasses the entire EEM region comprising emission scans and not just a single emission scan (Chen et al., 2003; Zhao et al., 2017). This investigation examined sources and fate of CDOM in brackish (31 lakes) and fresh lakes (32 lakes) across the TQP using EEM-FRI. The specific objectives are to: (1) characterize the differences in FDOM among the brackish and fresh lakes using EEM-FRI technology; (2) evaluate the dynamics of each FDOM components under spatial variations; (3) link the five-FDOM components by FRI with CDOM parameters and water quality; and (4) assess the effects on FDOM caused by salinity, solar radiation and land cover.

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2. Materials and methods 2.1 Overview of lakes on the TQP As the highest and extensive plateau in the world, the TQP in China covers an area about 2.5 million km2. Total lake areas on the TQP account for about 49% in China (Zhang et al., 2011), which store about 68% of lakes waters in China (Song et al., 2018b). Generally, lakes of the TQP are mainly formed by erosion and melting of glaciers, tectonic activity, or barriers present on the land-surface. The TQP has unique environmental characteristics, e.g., high elevation, low atmospheric density, low solid impurity content and water vapor, less cloud and low ozone concentration. These attribute to a higher dose of solar radiation (Fig.S1). Further, the TQP also has more prolonged sunshine duration and sunny days in summer than other regions in China (Fig. S1). Li et al. (2011) reported that TQP has an average annual solar radiation intensity about 6000-8000 MJ/m2, the highest region in China and second worldwide after the Sahara Desert. Annual precipitation in this region ranges from 100 to 1300 mm. The majority of precipitation occurs during the summer period (June to September). Owing to high altitude and thin air, this region has less atmospheric counter radiation. Thus, the average temperature is lower than other regions in China even in summer season (Fig. S1). 2.2 Field samplings Most of lakes in the TQP are in the depopulated zone in North Tibet, and it is a natural reserve for wild animals. Due to the geographical conditions and government management, it is difficult to reach these remote lakes. Thereby, we selected representative lakes based on their sizes, a priori knowledge (Wang and Dou, 1998), accessibility and salinity gradient. A total of 244 water samples were collected in ice free seasons (June to September) from 63 lakes across the TQP from 2014 to 2017. 6

Some lakes were revisited for multiple times, and some were just once due to the remote distances and less accessibility (Table S1). Lakes were divided into two groups, e.g., 32 fresh lakes (N=135) and brackish lakes (N=109), according to salinity/electrical conductivity (Table S1). Sample locations for each lake were recorded using a GPS receiver (Fig. 1). More details about the name, size (surface area), elevation, latitude/longitude, land-cover type and retention-time of each lake can be found in Table S1. Water samples were collected from lake surfaces (0-50 cm) with two 1-L acid-cleaned plastic bottles. The filtering process was conducted in the field, and these filtered samples were filled into brown plastic bottles every day. The bulk samples and filtered samples were transported back to the laboratory with portable ice cooler with ice packs. These filtered samples were stored at 4°C until analysis within 2 days in the laboratory. [Insert Figure 1 about here] 2.3 Water quality measurements Water turbidity was determined using the Shimadzu UV-2600 PC spectrophotometer with a matching 3 cm quartz cells at room temperature (20℃±2) and Milli-Q water as the reference (UV Talk Letter, 2013; Zhao et al., 2016). Electrical conductivity (EC) was measured using a portable multi-parameter water quality analyzer (YSI EXO1, US). Samples were first filtered through a pre-combusted Whatman GF/F filter (pore size, 0.7 μm) under a low vacuum in the field. Then the DOC concentrations were determined by high-temperature catalytic oxidation (680 ℃) using a total organic carbon analyzer (TOC-VCPN, Shimadzu, Japan) in the laboratory. Potassium hydrogen phthalate was used in DOC analysis as a reference. Chlorophyll a (Chl-a) analysis was undertaken by initially filtering the water samples through Whatman 7

cellulose acetone filters (0.45µm). The filters were extracted with 90% acetone and measured at 664, 647, 630 and 750 nm wavelengths using a Shimadzu UV-2006 PC spectrophotometer. Total suspended matter (TSM) was determined by gravimetrical analysis (Song et al., 2013). Total nitrogen (TN) and Total phosphorus (TP) were measured following methods highlighted in standard methods (APHA, 1998).

2.4 CDOM absorption measurements and parameters The collected water samples were first filtered through a pre-combusted Whatman GF/F filter (0.7 μm). Then they were further filtered through a pre-rinsed 25 mm Millipore membrane cellulose filter (0.22 μm) into brown plastic bottles (Zhao et al., 2016). In the laboratory, the absorption spectra of filtered samples were measured between 200 and 800 nm at 1 nm increments using a Shimadzu UV-2006 PC spectrophotometer with a 1 cm (or 5 cm) quartz cuvette and Milli-Q water as a reference. The absorption coefficient a(λ) was calculated from CDOM absorption OD(λ): a(λ) = 2.303OD(λ)/γ

(1)

where, OD(λ) is the corrected optical density at wavelength λ; γ is the cuvette path length (0.01 or 0.05 m); and the factor of 2.303 converts the results from a base 10 to a base natural logarithm (Zhang et al., 2011). 2.5 Excitation-emission matrix (EEM) fluorescence Three-dimensional excitation-emission matrix (EEM) spectra of CDOM were measured at room temperature (20±2 °C) using a Hitachi F-7000 fluorescence spectrometer with a 700 volt xenon lamp. Scanning band pass widths of excitation and emission spectra were obtained using wavelengths of 220-450 nm (with intervals of 5 nm) and 250-600 nm (1 nm intervals), respectively, with a scanning speed of 8

2400 nm·min-1. A Milli-Q water blank was analyzed, the result of which was subtracted from the resulting EEM of the water sample spectrum to eliminate Raman scatter peaks. In order to eliminate the inner filter effect, the EEMs were normalized by subtracting the integral area under the curve of the Milli-Q water Raman peak (Zhang et al., 2010; Zhou et al., 2016). These EEM spectra were then calibrated in quinine sulfate units (QSU) (Lawaetz and Stedmon, 2009). 2.6 CDOM absorption parameters and fluorescence indices According to Table S2, UV absorption at 254 nm normalized to DOC concentration called specific UV absorbance (SUVA254), was strongly correlated with CDOM aromaticity (Weishaar et al., 2003). M (E250:E365) defined by the ratio of CDOM UV absorption at 250 nm and 365 nm, which is denoted as M (E250:E365). It provides further insights into the average characteristics (chemistry, source and diagenesis) of CDOM than absorption values alone (De Haan and De Boer, 1987). In addition to the above parameters, CDOM absorption spectral slopes (S) at 275 – 295 nm was used to describe the ratio of fulvic acids to humic acids (Gonnelli et al., 2013; Helms et al., 2008). The humification index (HIX) was calculated from fluorescence EEMs, as indices for the humification degree and DOM sources (Zhang et al., 2010). Then the fluorescence indices FI370 and FI310 introduced by McKnight et al. (2001), were used to characterize CDOM source. More details of absorption and fluorescence parameters could be found in Table S2. 2.7 EEM fluorescence regional integration EEM Fluorescence Regional Integration (EEM-FRI) divides EEM boundaries into five regions associated with humic-like, tyrosine-like, tryptophan-like or phenol-like organic compounds (Chen et al., 2003). More details about the peak locations and descriptions could be found in Table S3. The integrated area beneath the EEM spectra 9

can be calculated using: (2)

φ𝑖 = ∫ex∫emI(λexλem)ΔλexΔλem

Where, Δλex is Ex (interval 5 nm); Δλem is Em (interval 1 nm); I (λex, λem) is fluorescence intensity at each EEM pair; and i represents the region of EEM divided by EEM-FRI (Table S3). The cumulative volume in the five regions beneath the EEM can be calculated using φT (i= I, II, III, IV, V; unit: nm): 5

(3)

φ T = ∑ i = 1φ i

where, i represents the numbers of cumulative region in the five regions (i= I, II, III, IV, V; Table S3). The cumulative volume beneath the EEM (φi and φT) values were normalized to per unit of DOC concentration (mg L-1) for comparison of EEMs from different sources (Table S3). The unit of DOC-normalized EEM-FRI is QSU-nm2-[mg/LC]-1. In addition, the percent fluorescence response in a specific region (Pi , i= I, II, III, IV, V) was calculated as: Pi,n =

φi

(4)

φT × 100%

2.8 Statistical analysis Statistical analyses were performed using the SPSS 16.0 (Statistical Program for Social Sciences). Regression and correlation analyses were conducted to examine the relationships between variables (φi of EEM-FRI, FI370 and a (350), HIX and a(350), DOC and a(254), DOC and FI370, DOC and φV) among lakes. Then r and R2 present the correlation coefficient and regression coefficient, respectively. Correlation analysis were conducted by t-test and 2-tailed (noted with

**,

p<0.01; *, p <0.05).

Statistical differences between variables were assessed with an ANOVA analysis. Significance levels are reported as insignificant (IS) (p>0.05), significant (noted with *,

0.05>p>0.01) or highly significant (noted with **, p<0.01).

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In order to assess variations in normalized cumulative volume φi by EEM-FRI among lakes, principal components analysis (PCA) based on correlation matrix of EEM-FRI was undertaken using CANOCO 4.5 (Microcomputer Power, Ithaca, NY, USA). Relationships between CDOM absorption, EEM-FRI fluorescent components, fluorescence indices (FI370, FI310 and HIX) and the water quality parameters were determined by redundancy analysis (RDA) using CANOCO 4.5 software package. CDOM spectroscopic indices (a(254), a(350), S275-295, SUVA254, M (E250:E365), FI310, FI370, HIX and φi (i=I, II, III, IV, V)) were selected as explanatory variables, and aquatic biogeochemical parameters (DOC, Chl-a, TSM, TN, TP, salinity and turbidity) were defined as species variables. In addition, the integrated land cover area of each basin was acquired to assess the relationship between CDOM fluorescence and land covers. Altogether, 20 basins (B1-B20) were extracted using a 30 m resolution DEM (Digital Elevation Model; http://www.gscloud.cn/).

3. Results 3.1 Biogeochemical characteristics Aquatic biogeochemical parameters (TN, TP, Chl-a, TSM, EC, turbidity and salinity) for the 63 lakes (244 water samples) were shown in Table 1. Altogether, 31 lakes were classified as brackish (N=109; 35‰>salinity >1‰) and 32 lakes were freshwater (N=135; salinity <1‰). In Table1, higher turbidity was shown in fresh lakes (16.7±43.61 NTU) than that in brackish lakes (14.63±24.40 NTU). The concentrations of TN (average, 2.31 ± 2.64 mg L-1), TP (average, 0.04 ± 0.03 mg L-1) and Chl-a (average, 1.45 ± 2.65 μg L-1) were relatively lower in fresh lakes (N=135). Likewise, brackish lakes were recorded with a high Chl-a (average, 2.57 ± 5.73 μg L-1), which related to their relatively high TN (average, 4.54 ± 4.32 mg L-1) and TP (average, 0.45 ± 1.35 mg L-1). 11

[Insert Table 1 about here] High DOC concentrations in brackish waters were found to accumulate in lakes with high salinity concentrations (Table. 2a). DOC values for the brackish lakes were also found to be variable, ranging from 0.27 mg L-1 in Lake XRC (23) to 164.8 mg L-1 in Lake CCL (1), with a mean DOC of 35.69 (± 43.52) mg L-1 (Table. 1). Mean DOC concentrations in the fresh lakes were 7.94 (± 12.17) mg L-1, recording lower values than those in brackish lakes. [Insert Table 2 about here] 3.2 CDOM absorption Generally, a (350) in our study ranged from 0.09-8.45 m-1 and 0-13.49 m−1 for brackish and fresh lakes, with mean values of 2.38 (± 3.14 SD) m−1 and 1.74 (± 1.99 SD) m−1, respectively (Fig. 2). Averaged SUVA254 values ranged from 1.47 (±2.55 SD) mg C-1 m-1 in brackish lakes to 2.29 (±1.36 SD) mg C-1 m-1 in fresh lakes (Fig. 2). ANOVA analysis indicated there are significant differences (p<0.05) between SUVA254 values and a(350) for brackish and fresh lakes. For brackish water lakes, M (E250:E365) ranged from 6.82 in Lake DJC (25) to 74.7 in Lake GMC (16), having an average value of 28.3± 20.3 for all brackish lakes. Results for M (E250:E365) in fresh lakes ranged from 5.7 in Lake TZC (62) to 89.5 in Lake LAC (37), with an average of 16.27 ± 20.6. Significant difference (p<0.05) in M (E250:E365) was revealed between brackish lakes and fresh lakes. As shown in Fig. 2, a significant higher S275-295 value (p<0.05) in brackish lakes (0.0380 ± 0.009 nm-1) were exhibited than that in fresh lakes (0.0324 ± 0.01 nm-1). [Insert Figure 2 about here] 3.3 Fluorescence indices 12

As shown in Fig. 3, FI370 in brackish lakes ranged from 0.11 (Lake PKC, 24) to 1.93 (Lake CCL, 1), with a mean value of 0.64 (±0.51 SD); FI310 ranged from 0.58 (Lake PKC, 24) to 1.93 (Lake GMC, 16), with a mean of 1.14 (± 0.36 SD). In contrast, FI370 of fresh lakes ranged from 0.11 (Lake TRC, 38) to 2.87 (Lake WZ-1, 36), with a mean value of 0.79 (±0.75 SD); FI310 of fresh lakes ranged from 0.57 (Lake WZ-1, 36) to 2.38 (Lake LAC, 37), with a mean value of 1.04 (± 0.43 SD), respectively. There are no significant differences between FI310 and FI370, implying the same CDOM sources for brackish and fresh lakes (p>0.05). However, the average HIX (3.15 ± 3.5) in fresh lakes showed a significantly higher (p< 0.05) than that in brackish lakes (1.8 ± 1.7). [Insert Figure 3 about here] 3.4 EEM-FRI and its difference analysis Typical EEMs of CDOM for four water samples obtained from lakes on the TQP were shown in Fig.4 (a-d). According to Chen et al. (2003), EMM-FRI divides the EEM signal into five regions (I, II, III, IV and V; Fig. 4a and Table S3). The excitation-emission area volumes φi (i= I, II, II, IV, V) and their proportion to total fluorescence intensity Pi (i= I, II, III, IV, V) were also shown in Fig.4 (e and f). A significant difference (p<0.05) of total fluorescence intensity φT was observed between the brackish and fresh lakes. In this study, φT ranged from 1.94 × 108 nm to 3.5 × 1010 nm for brackish lakes, with an average of 1.44 × 1010 nm (±8.1× 109 SD). As for fresh lakes, φT ranged from 3.54 × 108 nm to 3.5 × 1010 nm with an average of 1.38× 1010 nm (±7.9× 109 SD). For all the samples, the area volume of φi in the five integrated regions identified by EEM-FRI were: φV (Humic-like) > φIII (Fulvic-like) > φIV (Microbial protein-like) > φI(Tyrosine-like) > φII(Tryptophan-like). Furthermore, a significant difference for the fluorescence intensities of φV and φIII was found in brackish lakes and fresh lakes (p<0.05). 13

The P V =62.4% (±14.6) in brackish lakes ranged from 34.1% in Lake GMC (16) to 96.8% in Lake CCL (1). Fresh lakes recorded a range of 32.8% (Lake GRC-2, 43) to 87.5% (Lake CLQ, 47), with an average PV of 53.7%±13.0. In addition, the fulvic-like fluorescence intensities also showed a significant difference (p<0.05) between PIII in fresh lakes (24.8%; ±7.4) and that in brackish lakes (15.9 % ±8.8). The fluorescence intensities for φIV (microbial protein-like) accounted for a greater proportion in brackish lakes (15.5% ±8.2) than that in fresh lakes (12.5% ±6.8). [Insert Figure 4 about here] FDOM components in different lakes showed spatial heterogeneity. In order to eliminate the influence of spatial heterogeneity, the cumulative volumes beneath the EEM (φi) values were normalized to a DOC concentration of 1 mg L-1 (Fig.5). The average normalized total fluorescence intensities φ*T in brackish lakes was 1.1×109 QSU-nm2-[mg L-1 C] (±8.8), with a maximum value of 3.3 ×109 QSU-nm2-[mg L-1 C] in Lake GZC (17) and a minimum value of 4.8×107 QSU-nm2-[mg L-1 C] in Lake QHH (7). The φ*T for fresh lakes ranged from 2.1 ×107 QSU-nm2-[mg L-1 C] in Lake TZC (62) to 9.5×109 QSU-nm2-[mg L-1 C] in Lake WRC (59), with an average φ*T of 3.3×109 QSU-nm2-[mg L-1 C] (±2.6). Significant difference of φ*T was observed between brackish and fresh lakes (p<0.001), which was opposite to the non-normalized EEM-FRI results exhibited in Fig.4e. [Insert Figure 5 about here] 3.5 PCA of normalized EEM-FRI Principal components analysis (PCA) indicated that factor 1 and factor 2 axes could explain 92.8% of total variance for EEM-FRI, accounting for 66.9% and 25.9%, respectively (Fig.6). The Kaiser-Meyer-Olkin analysis showed that the statistical 14

magnitude was larger than 0.8, thus the five normalized EEM-FRI fluorescent components exhibited positive factor 1 loadings (Fig. 6a). Factor analysis showed PCA factor 1 and factor 2 was associated with five cumulative volumes φ*i (i=I, II, III, IV, V) in a linear formula. Factor 1 and factor 2 can be expressed as: factor 1= -0.543φ*I-1.35φ*II+0.788φ*III+0.856φ*IV+0.98φ*V factor 2= 0.899φ*I+1.78φ*II-0.559φ*III-0.636φ*IV-0.774φ*V. φ*III (fulvic-like), φ*V (humic-like) and φ*IV (microbial protein-like) showed a positive factor 1 loading, and concurrently showed negative factor 2 loading. This correlation result indicated that PCA could separate normalized cumulative volume φi by EEM-FRI into two groups: Group 1 (φ*III, fulvic-like; φ*V, humic-like; φ*IV, microbial protein-like) and Group 2 (φ*I, tyrosine-like; φ*II, tryptophan-like). This finding was contrary to the results from Zhao et al. (2017), Yao et al. (2011) and Yamashita et al., (2010) from other water bodies. [Insert Figure 6 about here] 3.6 Correlation analysis of CDOM spectroscopic indices Strong correlations between tyrosine-like φ*I and tryptophan-like φ*II in fresh (r= 0.93**, R2=0.86; t-test, p<0.01; N=135) and brackish lakes (r= 0.90**, R2=0.80; t-test, p<0.01; N=109) were revealed. This suggested that they may have similar sources (Fig. 7a). A moderate correlation between φ*I and microbial protein-like φ*IV was observed in brackish lakes (r=0.84**, R2=0.70; t-test, p<0.01; N=109), and a weak correlation was recorded in fresh lakes (r=0.76**, R2=0.57; t-test, p<0.01; N=135) (Fig. 7b). It demonstrated that parts of the two FRI fluorescent components might have some common sources in brackish lakes. However, strong correlations between tryptophan-like φ*II and microbial protein-like φ*IV were not observed, which was consistent with the results of Chen et al. (2003) and Zhao et al. (2017). 15

Furthermore, EEMs can be divided into two groups in the PCA results (Fig. 6). A positive correlation between φ*III&IV&V and φ*I&II (r= 0.87**, R2 = 0.76; t-test, p<0.01; N= 109) in brackish lakes (Fig. 7c) was revealed, while a moderate correlation was revealed in fresh lakes (r= 0.74**, R2 = 0.54; t-test, p < 0.01; N= 135) (Fig. 7d). In addition, close correlations between the a (350), FI370 and HIX were observed in the fresh lakes (r=0.79**, R2>0.66; t-test, p<0.01; N= 135) (Fig. 7 e and f). In brackish lakes, a close association was exhibited between a(350) and HIX (r=0.80**, R2=0.65; t-test, p<0.01; N= 109). [Insert Figure 7 about here] 3.7 CDOM versus water biogeochemistry Redundancy analysis (RDA) results showed that species–environment correlations of brackish and fresh lakes were 0.41 and 0.28, respectively (Fig.8). For brackish lakes (N=109), the first two RDA axes accounted for 80.7 % of total water biogeochemical variability (axis one, 45.5 %; axis two, 35.2 %). Coefficients between environmental variables with RDA axes indicated that a (254), a (350) and HIX were correlated with water biogeochemical parameters (TN, TP, DOC, TSM, Chl-a, salinity and turbidity), followed by S275-295 and M(E250:E365). For the fresh lakes (N=135), the first two RDA axes accounted for 75.5 % of total variability (axis one, 59.4 %; axis two, 16.1 %). Similarly, a (254), HIX and a (350) were correlated with water quality parameters (TN, TP, DOC, TSM, Chl-a, salinity and turbidity), followed by FI370 and normalized φ*IV. [Insert Figure 8 about here] In addition, correlation and regression analysis were undertaken between DOC concentration and normalized cumulative volume φ*i (i=I, II, III, IV, V) for all water 16

samples (Table 3). Brackish lakes was divided into four groups with salinity as the criterion: salinity >10‰ (average EC 23764 μs cm -1), salinity >6‰ (average EC 10945 μs cm -1), salinity >2‰ (average EC 5708 μs cm -1), and salinity >1‰ (average EC 2119 μs cm -1). Generally, salinity <1‰ was consistent with that of fresh lakes (average EC 586 μs cm -1). Moderately positive correlations between the normalized cumulative volume φ*i (i=I, II, IV, V) and DOC concentration were exhibited. [Insert Table 3 about here] 3.8 CDOM fluorescence versus land cover Twenty basins (B1-B20) were extracted to assess the relationship between FDOM fluorescence and land cover. The proportion of different land cover types to total basin area was shown in Fig.S3. On the TQP, grassland with plentiful organic-rich ecosystems were the major land-cover type (Fig. S3). CDOM spectroscopic indices of lake samples in each basin were averaged to analyze the influence of land cover. The results showed a moderate correlation between DOC and normalized humic-like φ*V for 20 basins (r=-0.46**, R2=0.54; t-test, p<0.01; Fig. 9d). Normalized φ*III, φ*IV and φ*V in basins with large grassland areas (average area 14876 km2; N=10 basins) exhibited higher values than in basins with small grassland areas (averaged area 1976 km2; N=10 basins) (ANOVA, p<0.05). Similar results were also found for forest and unused land, although they only accounted for small proportions of total area in each basin (Fig. 9). [Insert Figure 9 about here]

4. Discussion 4.1 The effect of EC/salinity on DOC and CDOM Comparing with fresh lakes (DOC, 7.94 ± 12.05 mg L-1), brackish lakes with longer 17

retention time showed higher DOC concentrations (35.69 ± 43.52 mg L-1) on the TQP (Table 1 and Table S1). Likewise, substantial variations for both DOC and CDOM spectroscopic parameters were also observed between the fresh and brackish lakes (p<0.05) (Table 1 and Fig. 2). Our results were in agreement with the findings of Song et al. (2013, 2017a), Zhao et al. (2016) and Wen et al. (2016) for brackish lakes in other arid and semi-arid regions. These results can be explained by evapo-concentration and accumulation processes of DOC and CDOM from terminal lakes in arid regions (Curtis and Adams, 1995; Song et al., 2013, 2017a). Spencer et al. (2014) addressed that DOC was also around 525-675 ybp (ybp, years before present) in Tibetan alpine lakes. They attributed this to either inputs of pre-aged organics from glacier runoff, direct deposition, or to the aging of organics in situ (i.e. a radiocarbon reservoir effect). High salinity has a direct or indirect impact on water quality of brackish lakes, and they highlighted different structures and composition of DOM (Wen et al., 2016). It was also found that the aquatic biogeochemical parameters of brackish lakes were slightly higher than the average values for brackish lakes in Northeast China (average, TP=0.11 mg L-1 and TN=4.07 mg L-1 from Zhao et al., 2017), and lakes (average, TP=0.033 mg L-1 and TN=0.59 mg L-1; ) in Yungui Plateau of China (Zhang et al., 2010), but lower than those in Hulun Lake (average, TP=1.52 mg L-1and TN=4.58 mg L-1; Wen et al., 2016). In addition, RDA results of brackish lakes also indicated that CDOM showed a relatively higher correlation with water quality (TN, TP, DOC, TSM, Chl-a, salinity and turbidity) (Fig. 8). Zhao et al. (2016) reported that the formed macromolecular structures of humic-like substances could regulate the solubility of ions and organic pollutants. However, with correlation and regression analysis, the affined characteristics of brackish lakes in our study area could be due to a weak connection between salinity

18

(EC) and DOC (un-exhibited; r=0.49** and R2=0.24, t-test, p<0.01). Comparably, opposite results from brackish lakes in the northeastern China plain (r=0.97**, R2= 0.93, p<0.01; Zhao et al., 2016) and on the Inner Mongolia Plateau (Only regression analysis R2= 0.72, p<0.01; Wen et al., 2016) have been previously noted. Generally, organic carbon with different sources (allochthonous or autochthonous) and composition may result in different relationships existing between DOC and salinity (EC) (Curtis and Adams, 1995; Spencer et al., 2012). In our study area, it indicated that regional hydrogeological and climatic conditions play an important role in driving DOC variability (Song et al., 2017a). Further, salinity was divided into four groups (>10‰; >6‰; >2‰; >1‰) in brackish lakes (Table 3). The normalized cumulative volume φ*i (φ*I, φ*II, φ*IV and φ*V) of water samples with a salinity >10‰ (average EC 23764 μs cm

-1)

by

EEM-FRI showed moderate correlations with DOC concentrations (r ranged from -0.51** to -0.86**; R2 ranged from 0.59 to 0.75). Both r and R2 values showed a consistent decreasing tendency with salinity or EC (Table 3). DOM could accumulate via soil leaching and runoff passing through various landscapes in terminal/brackish lakes (Song et al., 2013). These allochthonous DOM could be available for the microorganisms, or be sink to the bottom, or be transformed into inorganic carbon (including CO2) (Cole et al., 2007; Tranvik et al., 2009). More allochthonous inputs make small humic-like molecules saturated in terminal/ brackish lakes and form colloidal particles (Chin et al., 1998). They could continue to form macromolecular structures (globular aggregates and ring-like) in high ionic strength environments (Myneni et al., 1999). In our highest salinity group, increasing salinity (EC) could increase DOC/CDOM solubility. By contrast, it can be seen that the inflow rivers of a specific lake generally showed lower DOC concentrations than that inside the lakes

19

(Fig. S3). In addition, the high salinity could also limit microbial activity due to a decrease of osmotic potential (Mavi et al., 2012). Pagano et al. (2014) verified that the high EC and alkalinity in brackish lakes was disadvantageous for microorganism production of autochthonous CDOM components. Arts et al. (2000) reported that increasing salinity could limit the microbial activity by reducing cell permeability. Therefore, DOC/CDOM accumulates in brackish (terminal) waters at significantly higher rates than those in fresh (open) waters (Duarte et al., 2005). In addition, P*i of EEM-FRI in brackish and fresh lakes also showed significant differences (p<0.001). Normalized humic-like (φ*V) and fulvic-like (φ*III) were terrestrial sources, accounting for P*III+V = 77.7% (±10.1 SD) in brackish lakes and 77.7% (±7.3 SD) in fresh lakes. Protein-like fluorescence, including tyrosine-like and tryptophan-like (φ*I+II), recorded a greater proportion in brackish water (P*I+II =6.47%; ±2.6 SD) than in fresh lakes (P*I+II =22.3%; ±4.0 SD) (Fig. 5 c and d). Likewise, we found a higher humic-like averaged percentage (P*V= 60.2%) by normalized EEM-FRI in brackish lakes compared with freshwater lakes (51.8%) (Fig. 5). This result implied that a greater formation of macromolecular structures from humic-like substances in brackish lakes prevail. 4.2 Solar radiation and elevation versus DOC and CDOM The TQP possesses a markedly high altitude and a thin atmosphere compared to other studies (Ren et al., 1997) (Fig. 1c and Fig.S1). These environmental factors may have an important influence on the photochemical oxidation processes of CDOM and the mineralization of DOC (Duarte et al., 2005; Tobias and Bohlke, 2011). Among the 63 lakes (N=244), the brackish lakes, for average M(E250:E365), S275-295 and SUVA254, were distinctly different from fresh lake results (N=135) (Fig.2). SUVA254 values for brackish lakes recorded lower values than those recorded in terminal water from the 20

Inner Mongolia Plateau (SUVA254=1.90±0.57 mg C-1 m-1) or in brackish lakes of Northeast China (2.8-5.7 mg C-1 m-1) (Zhao et al., 2016; Wen et al., 2016). This indicated that brackish lakes on the TQP were predominated with lower aromatic moieties of CDOM and average molecular weight of DOC compared within fresh lakes. The average M(E250:E365), S275-295 and SUVA254 in the brackish lakes (N=109) showed similar results of Boehme et al. (2004) in the Gulf of Mexico. In addition, our results also highlight significant differences in total fluorescence intensities φT and φ*T between brackish lakes and fresh lakes (p<0.001) (Fig. 4 and Fig. 5), respectively. We found that the average normalized total fluorescence intensities φ*T between brackish and fresh lakes showed opposite vitiation tendency. This finding may account for relatively higher colorless DOC present in the brackish lakes compared with the fresh lakes, which is a function of solar radiation and prolonged hydraulic retention time (Table S1 and Fig.S1). Song et al. (2017b) reported the regression model slopes for DOC vs. aCDOM (275) ranged from extremely low 0.33 (highly saline lakes) to 1.03 (urban waters) and 3.01 (river waters). Their results demonstrated that strong photo-degradation due to less cloud cover, longer water residence time, and sunshine hours on the TQP. In order to evaluate the influence of solar irradiance to CDOM optical characteristics (Fig. 10), solar irradiance was divided into three groups (>2900 h; >2800 h; >2600 h). Strong solar radiance and time could accelerate chromophores associated with high molecular weight being reduced (McKnight et al., 2001; Hu et al., 2017). By chemical bond rupture, high molecular weight substances were transferred to low molecular weight pools. This photolysis process could be accelerated by a prolonged hydraulic retention time and intensive solar radiation (Hu et al., 2017; McKnight et al., 2001; Song et al., 2017b).

21

However, for water samples with a solar radiance >2900 h (averaged solar irradiance hours), DOC recorded a moderate positive correlation with a(254) concentrations (r=0.87**, R2 =0.76; t-test, p<0.01), and a correlation with FI370 (r=0.69**, R2 =0.48; t-test, p<0.01). This indicated that, on the TQP with intensive UV-B radiation (solar irradiance, >2900 h), parts of the CDOM (mainly from allochthonous inputs) have similar sources with DOC. The PCA result of φ*i exhibited that φ*IV (microbial protein-like) was consistent with φ*V (humic-like) and φ*III (fuvic-like) (Fig. 6). This suggests that they have common sources. Likewise, a positive correlation (N= 109) between the total normalized cumulative volume φ*III&IV&V and φ*I&II (r=0.87**, R2 = 0.76; t-test, p<0.01) were found in brackish lakes. These results also demonstrated that microbial protein-like FDOM in brackish lakes associated with products from terrestrial microbial decomposition (Fig. 7). Zhang et al. (2013) also identified correlations between total bacterial community structure and altitude on the TQP. For the lakes on the TQP, sources and compositions of CDOM exhibited a similar to those in marine environment. Intensive solar radiance has the potential to enhance photochemical degradation of allochthonous CDOM on the TQP. This process resulted in absorption parameters with the production of low molecular weight CDOM. These findings are contrary to those recorded from rivers in intermontane plateaus in the USA (Spencer et al., 2012), lakes in the Songnen Plain, China (Song et al., 2013), Hulun Lake, China (Wen et al., 2016) and basin rivers in China (Zhao et al., 2016). In arid and semi-arid regions, brackish lakes commonly support highly active biological communities. They can actively break down refractory organic matter into DOC and accumulate in waters (Wen et al., 2016). However, due to strong UV-B radiation on the TQP, long sunlight duration may

22

result in photobleaching of CDOM which will limit microbial activities and increase mineralization of DOC (Granéli et al., 1996; Duarte et al., 2005). Differences in results from our study and previous investigations may be due to the majority of the microbial protein-like FDOM being derived from terrestrial microbial decomposition. Water samples from brackish lakes were generally distributed in the range of -1 to 1 for both PCA factor scores. This finding confirms that the contributions of allochthonous substances (including microbial protein-like) were obvious in brackish lakes. In addition, PCA results could be attributed to both source differences and photo-degradation process. The characteristics of CDOM and DOC in brackish lakes on the TQP is similar to that in marine environments. Zhang et al. (2013) reported that the majority of lakes on the TQP were affiliated with SAR11-III clade, similar to observations from Chesapeake Bay bacterial plankton. It suggests that solar radiation has an important effect on CDOM photo-absorption characteristics, and it also contributes to DOC variability and fate. [Insert Figure 11 about here] 4.3 The effect of land-cover on DOC and CDOM Fig.9 indicated that TQP land covers are associated with CDOM and DOC, but it is not as important as the effects of salinity and solar radiation do. The TQP is located in an arid climatic zone with low rainfall, and the inflow of lakes mainly depends on surface runoff. Generally, grasslands and forests are characterized by high nitrogen and organic matter export rates (Heinz et al., 2015). High DOC concentrations in the lake waters highlight the organic-rich nature of these ecosystems. These results can be explained by DOC flux related to physical/chemical properties, hydrology, and land cover within a specific drainage watershed for each lake (Heinz et al., 2015; Li et al., 2018). Table S4 listed the temporal variations and ANOVA of averaged CDOM and 23

DOC characteristics from lakes with twice sampling period in TQP. In most of the case, no significant difference was observed in different sampling years except Lake XingXingHai, which is a shallow lake and strongly disturbed by herding cattle and sheep. Thus, CDOM and DOC may show fluctuations due to variation of slope runoff. As a result of climatic and geographical conditions, these environment factors may change the optical characteristic of CDOM and water quality on the TQP. However, it should be highlighted that hydrology, retention time, and strong solar radiation are the key factors for lake CDOM and DOC variations on the TQP. 4.4 Limitation and future implications The TQP is a unique geographic environmental with less anthropogenic activities, high elevation and strong ultraviolet radiation. In their dry environments, terminal/brackish lakes generally exhibit high salinity due to the evapocondensed effect. This signified that there were a accumulation in DOC concentrations and less unit CDOM. These detailed DOC and CDOM characteristics data in this study is helpful to improve and understand our knowledge of lakes carbon characteristics on the TQP. However, Timko et al. (2015) found that pH effects on FDOM also attibuted to the measurements and irradiation experiments of DOM, as well as dissolved iron concentrations and other water quality parameters. Investigations have shown that pH could change the charge of molecules by protonation/deprotonation for inland waters (Sutton and Sposito, 2005; Timko et al., 2015). Given the variations of pH in fresh or brackish lakes, further systematic examination of pH and FDOM is needed in future studies. Paralleled with investigations by others, the CDOM/DOC characteristics linked with various and dynamic changes of environmrntal factors, e.g., eutrophication status, sediments and underground waters (Song et al., 2018a, 2018b; Zhang et al., 2010; 24

Zhou et al., 2016). Only the spatial variations of CDOM/DOC were analyzed with respect to arid environments on the TQP in this work. Although there were only temporal differences between FDOM parameters in-lakes (Table S4; p>0.05). Terrestrial inputs by hyrological processes in temporal scales is also important for carbon flux, which is worth to investigate in the further study. As demonstrated by this study, CDOM and DOC in brackish lakes in the study area is similar to that in marine environments. Thus, it should be noted that the dynamic carbon role of saline lakes is worthy of future consideration in a view of termporal and spatial. It is also a challage that an interface between converging environmental factors with comprehensive data sets, particularly for those waters located in remote areas and harsh environments with less accessibility (Song et al., 2018a).

5. Conclusions High elevation, strong ultraviolet radiation, long sunshine hours, arid climate and terminal waters with low anthropogenic impact make the unique environmental conditions for CDOM and DOC studies on the TQP. Currently, little is known about DOC/CDOM fluorescence and its relationship with aquatic biogeochemical characteristics in lakes across the TQP. In order to fill this study gap, 244 samples collected from brackish and fresh water lakes across the TQP to examine DOC/CDOM spatial variation and its link with environmental factors. EEM-FRI was applied to characterize CDOM spatial variations between brackish lakes (salinity>1‰) and fresh lakes (salinity<1‰). Significant differences of CDOM spectroscopic indices, normalized φ*T and DOC concentrations were found between fresh and brackish waters (p<0.05), and lower average molecular weight of DOM in brackish lakes are prevailing. This investigation revealed that significant correlations were observed between the average DOC and CDOM fluorescence with salinity >10‰. However, 25

the correlation coefficients exhibited a decreasing tendency with salinity lower than 10‰. Further, significant correlations between the average DOC and a(254) with annual total sunshine hours > 2900 h were also observed, and also a strong association between DCO and FI370. While, the r and R2 values of regression analysis had a decreasing tendency with sunshine hours less than 2900 h. Finally, moderate relationship between normalized φ*V and DOC concentration was observed (r=-0.46**, R2=0.54; t-test, p<0.01) for different land cover types (mainly grass) in 20 basins across the TQP. Our findings also highlight that salinity, solar hours and land cover primarily contribute to CDOM and DOC properties in lakes on the TQP. These special characteristic of CDOM and DOC from brackish lakes in the study area is similar to that tracked in marine environments.

Acknowledgments. The research was jointly supported by the National Natural Science Foundation of China (41730104) and the “Hundred Talents Program” from Chinese Academy of Sciences granted to Dr. Kaishan Song. The authors also would like to thank the anonymous reviewers for their instructive and valuable comments that really strengthened this manuscript.

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33

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34

Table 1 Water quality parameters of samples from 63 lakes (N=244) on the TQP and ANOVA Parameters

Brackish Lakes (N=109)

Fresh Lakes (N=135)

p

Mean ±S.D.

Min-Max

Mean ±S.D.

Min-Max

Turbidity

14.63±24.40

0-87.78

16.7±43.61

0-212.51

0.717

EC

8880.23±8235.91

1673-33141.2

536.55±332.29

120.1-1369.20

0.000

Salinity

6.01±5.60

1.14-22.54

0.36±0.22

0.08-0.93

0.000

TN

4.54±4.32

0.31-15.56

2.31±2.64

0.16-10.15

0.011

TP

0.45±1.35

0.006-6.79

0.04±0.03

0.001-0.08

0.011

Chl-a

2.57±5.73

0-31.37

1.4±2.68

0.09-14.68

0.455

DOC

35.69±43.52

0.27-164.80

7.94±12.17

1.84-67.79

0.000

TSM

8.41±13.88

0-80.00

5.41±15.10

0-129.23

0.156

TN, TP, DOC, and TSM represent total nitrogen, total phosphorus, dissolved organic carbon and total suspended matter, respectively (mg L-1). EC represents the electrical conductivity of water samples (μs cm -1). Chl-a, chlorophyll-a concentration (μg L-1). The unit of turbidity is NTU (nephelometric turbidity unit), nephelometric turbidity unit, and salinity is ‰. p is the ANOVA result of water quality parameters from brackish lakes and fresh lakes, and p<0.05 indicates significant differences between variables.

35

Table 2 The Averaged DOC & salinity (a) and DOC & elevation (b) from 63 lakes in TQP (a)

Averaged

Lake Name

Salinity

Abb.

(‰)

CCL (1)

22.5

164.8

TSH (2)

19.4-22.8

JC (3)

N

(b)

Averaged

N

Lake Name

Elevation

Abb.

(m)

1

DJC (25)

5151

11.6

2

40.1

11

PMYC (55)

5024

3.3

4

12.2-13.5

16.9

3

LC (30)

4944

9.9

2

HLH(4)

12.2-12.4

12.4

3

GRC (41)

4863

8.3

1

DZC (5)

11.7-11.9

135.4

10

TZC (62)

4837

3.6

1

DOC (mg

L-1)

DOC (mg L-1)

DBC (6)

11.4

151.3

1

CRC (32)

4807

9.6

1

QHH (7)

9.64-10.12

17.2

2

GZC (17)

4799

78.4

1

PC (8)

8.0-8.5

75.7

4

CLQ (47)

4743

29.3

1

ZRNMC (9)

6.5-7.8

5.5

7

WZ-3 (58)

4742

11.5

11

SLC (10)

6.2-7.7

19.0

11

BMLMC (21)

4729

13.9

3

DBC-2 (11)

6.6-7.4

9.5

2

NMC (33)

4729

4.4

15

AZC (12)

6.5

73.9

1

XRC (23)

4723

0.3

1

BMC (13)

6.2-7.2

32.3

9

WZ-1(36)

4715

67.8

1

DRYC (14)

5.8-5.9

21.1

4

SLC (10)

4701

19

11

ARJC (15)

4.65-4.68

69.3

3

CJC (27)

4677

9.2

2

GMC (16)

4.6

14.33

1

QGC (28)

4671

11.3

2

GZC (17)

3.7

78.35

1

GRC-2 (43)

4664

4.5

1

ZNC (18)

3.6

17.04

1

DBC-2 (11)

4660

9.5

2

GH-1 (19)

2.4

27.93

1

DBC (6)

4656

151.3

1

WZH (20)

1.9-3.0

5.6

3

GRC-3 (60)

4654

2.5

6

BMLMC (21)

2.3-2.4

13.9

3

CCL (1)

4624

164.8

1

ELA(22)

2.1

4.34

1

ZRNMC (9)

4617

5.5

7

XRC (23)

1.9

0.27

1

GMC (16)

4617

14.3

1

PKC (24)

1.84-1.87

25.8

4

ZNC (18)

4616

17

1

DJC (25)

1.7-1.9

11.6

2

CBG (53)

4612

6.9

1

GH-2 (26)

1.7

20.94

1

JC (3)

4608

16.9

3

CJC (27)

1.55-1.56

9.21

2

MPYC (50)

4600

2.6

2

QGC (28)

1.3-1.6

11.3

2

CN (44)

4590

4.7

7

YH (29)

1.16-1.23

8.1

11

PKC (24)

4585

25.8

4

LC (30)

1.1-1.3

9.9

2

LAC (37)

4575

11.8

4

YH-2 (31)

1.1

3.90

1

SBC (63)

4573

4

1

CRC (32)

0.9

9.56

1

TRC (38)

4572

3.5

4

NMC (33)

0.7-0.9

4.4

15

CE (57)

4570

7.7

4

XWR (34)

0.73

1.94

1

BMC (13)

4565

32.3

9

KLKH (35)

0.6-0.7

5.8

13

QGC-2 (52)

4561

2.8

1

WZ-1(36)

0.68

67.8

1

WRC (59)

4559

2.5

5

LAC (37)

0.64-0.76

11.8

4

ELA(22)

4557

4.3

1

TRC (38)

0.51-0.52

3.5

4

AZC (12)

4544

73.9

1

BGC (39)

0.50-0.54

3.1

5

DRYC (14)

4540

21.1

4

HTTL (40)

0.48

2.03

1

PC (8)

4534

75.6

4

36

GRC (41)

0.48

8.3

1

DZC (5)

4470

135.4

10

DJCN (42)

0.34-0.35

3.6

4

KTC (61)

4452

1.9

1

GRC-2 (43)

0.3

4.5

1

YH (29)

4447

8.1

11

CN (44)

0.29-0.3

4.7

7

ARJC (15)

4303

69.3

3

ZLH (45)

0.24-0.35

8.4

7

ZLH (45)

4295

8.4

7

XXH (46)

0.21-0.41

10.3

7

ELH (49)

4273

5

6

CLQ (47)

0.28

29.3

1

BGC (39)

4244

3.1

5

LJX-1 (48)

0.26-0.28

1.8

10

XXH (46)

4237

10.3

7

ELH (49)

0.25-0.26

5.0

6

WZ-2(51)

4236

12.6

2

MPYC (50)

0.25

2.6

2

DJCN (42)

4088

3.6

4

WZ-2(51)

0.22-0.27

12.6

2

HLH(4)

4081

12.4

3

QGC-2 (52)

0.24

2.83

1

XWR (34)

3825

1.9

1

CBG (53)

0.24

6.90

1

GH-1 (19)

3204

27.9

3

LJX-2 (54)

0.22-0.23

2.4

6

QHH (7)

3199

17.2

2

PMYC (55)

0.22-0.24

3.3

4

HTTL (40)

2922

2

1

LYX (56)

0.20-0.21

2.7

11

GH-2 (26)

2859

20.9

1

CE (57)

0.15-0.22

7.7

4

KLKH (35)

2817

5.8

13

WZ-3 (58)

0.19

11.54

1

TSH (2)

2808

40.1

11

WRC (59)

0.18-0.22

2.5

5

WZH (20)

2756

5.6

1

GRC-3 (60)

0.16-0.18

2.5

6

YH-2 (31)

2695

3.9

1

KTC (61)

0.16

1.91

1

LYX (56)

2572

2.7

11

TZC (62)

0.14

3.64

1

LJX-2 (54)

2164

2.4

6

SBC (63)

0.08

4.02

1

LJX-1 (48)

1733

1.8

10

Lake names can be found in Table S1. The 63 lakes in descending order accorded with salinity (a) and elevation(b).

37

Table 3 Correlation and Regression analysis equations of DOC concentration and log- normalized cumulative volume φ*i (i=I, II, III, IV, V) for all the water samples from 63 lakes (N=244) on the TQP Salinity

Averaged EC

Regression equation

r

R2

DOC & Log-φ*I (Tyrosine like) >10

23764

y=7.43e-9E-04x , (N=29)

-0.85**

0.74

>6

10945

y=7.49e-9E-04x , (N=64)

-0.65**

0.44

>2

5708

y=7.54e-9E-04x , (N=84)

-0.59**

0.34

>1

2119

y=7.58e-9E-04x , (N=109)

-0.58**

0.34

<1

586

y = 8.08e-0.002x , (N=135)

-0.19*

0.03

DOC & Log-φ*II (Tryptophan like) >10

23764

y=7.15e-8E-04x , (N=29)

-0.81**

0.65

>6

10945

y=7.22e-8E-04x , (N=64)

-0.65**

0.42

>2

5708

y=7.27e-8E-04x , (N=84)

-0.56**

0.32

>1

2119

y=7.36e-9E-04x , (N=109)

-0.59**

0.34

<1

586

y = 7.90e-0.001x, (N=135)

-0.18*

0.03

DOC & Log-φ*III (Fulvic like) >10

23764

y = 7.92e-5E-04x, (N=29)

-0.51*

0.26

>6

10945

y = 8.13e-6E-04x, (N=64)

-0.39*

0.15

>2

5708

y = 8.22e-5E-04x, (N=84)

-0.28*

0.07

>1

2119

y = 8.22e-6E-04x, (N=109)

-0.28*

0.08

<1

586

y =8.83e-0.001x, (N=135)

-0.13

0.01

DOC & Log-φ*IV (Microbial protein like) >10

23764

y=8.02 e-6E-04x , (N=29)

-0.78**

0.61

>6

10945

y=8.11 e-7E-04x , (N=64)

-0.62**

0.39

>2

5708

y=8.14 e-7E-04x , (N=84)

-0.52**

0.27

>1

2119

y=8.15 e-7E-04x , (N=109)

-0.53**

0.28

<1

586

y =8.54e-0.002x, (N=135)

-0.15

0.02

-0.77**

0.60

DOC & Log-φ*V (Humic like) >10

23764

y=8.56 e-4E-04x , (N=29)

38

>6

10945

y=8.57 e-5E-04x , (N=64)

-0.53**

0.28

>2

5708

y=8.64 e--5E-04x , (N=84)

-0.47**

0.22

>1

2119

y=8.67e--5E-04x , (N=109)

-0.50**

0.25

<1

586

y =9.18e-9E-04x, (N=135)

-0.16

0.02

DOC & Log-φ*III&VI&V (Humic like& Microbial protein like & Fulvic like) >10

23764

y = 8.72e-5E-04x, (N=29)

-0.76**

0.59

>6

10945

y = 8.77e-5E-04x, (N=64)

-0.55**

0.30

>2

5708

y = 8.85e-5E-04x, (N=84)

-0.49**

0.25

>1

2119

y =8.88e-6E-04x , (N=109)

-0.51**

0.26

<1

586

y = 9.42e-0.001x, (N=135)

-0.17*

0.03

DOC & Log-φ*I&II (Tyrosine like & Tryptophan like)

*,

>10

23764

y = 7.63-8E-04x, (N=29)

-0.86**

0.75

>6

10945

y = 7.69e-8E-04x, (N=64)

-0.68**

0.47

>2

5708

y = 7.71e-8E-04x, (N=84)

-0.57**

0.33

>1

2119

y =7.76e-9E-04x , (N=109)

-0.58**

0.34

<1

586

y =8.30e-0.002x , (N=135)

-0.19*

0.03

0.05>p>0.01; **, p<0.01. The unit of EC is μs cm -1; salinity is ‰; DOC concentration is mg L-1; φi (i=I, II, III,

IV, V) is QSU-nm2-[mg L-1 C].

39

Figure 1. (a) Map of sampling locations from lakes on the TQP with various land use/land cover types; (b) The elevation (m) of TQP; (c) Sunshine duration characteristics for TQP. The total sunshine hours in 2016 were from China Metrological Data Sharing Service System (http://data.cma.cn/). Sunshine hour data were averaged, interpolated and mapped in ArcGIS 10.0 by Geostatistical analysis in the TQP. Then we used ‘Extract Multi Values to Points tool’of Arc toolbox to acquire the sunshine hours from each lake sample. Referring to the natural breaks method of Geostatistical analysis and data iteration method, those lake samples were divided into three groups to evaluate the influence of solar irradiance to CDOM optical characteristics.

40

Figure 2. Box plots of a(254) (a), a(350) (b), M(E250 ： E365) (c), S275-295 (d) and SUVA254 (e) for brackish and fresh lakes on the TQP. The balls beside the boxes are the lake samples. The black line and the blue squares in the boxes represent the median and mean values, respectively. The horizontal edges of the boxes denote the 25th and 75th percentiles; the whiskers denote the 10th and 90th percentiles.

41

Figure 3. Box plots of HIX (a), FI370 (b) and FI310 (c) for brackish and fresh waters on the TQP. The balls beside the boxes are the lake samples. The black line and the blue squares in the boxes represent the median and mean values, respectively. The horizontal edges of the boxes denote the 25th and 75th percentiles; the whiskers denote the 10th and 90th percentiles.

42

Figure 4. Four typical EEM fluorescence spectras (a-d) and FRI results, (a) Lake DZC (5), (b) Lake BMC (13), (c) Lake BMLMC (21), (d) Lake NMC (33), (e) The proportion and cumulative volume proportion of EEMFRI-extracted average FDOM components from five regions in brackish lakes and fresh lakes on the TQP, and (f) Distributions of percentages of EEM-FRI extracted FDOM.

43

Figure 5. Normalized EEM-FRI fluorescence component and spatial characteristics from 63 lakes in Tibet Plateau, (a) Normalized cumulative volume φ*i of EEM-FRI extracted average FDOM components from five regions in brackish lakes and fresh lakes, (b) Percentages P*i of Normalized EEM-FRI extracted FDOM in brackish lakes and fresh lakes, (c) Spatial distributions of normalized cumulative volume φ*i in brackish lakes (1-31) and fresh lakes (32-63) and (d) Spatial distributions of percentages P*i of EEM-FRI extracted FDOM in brackish lakes (1-31) and fresh lakes (32-63).

44

Figure 6. PCA results of normalized cumulative volume φ*i by EEM-FRI. (a) Loadings of PCA factors and (b) Property-property plots of PCA factor scores. The unit of normalized cumulative volume φ*i (i=I, II, III, IV, V) is QSU-nm2-[mg L-1 C].

45

Figure 7. The correlations between normalized cumulative volume φ*I and φ*II by EEM-FRI for water samples in brackish lakes and fresh lakes (a); Normalized φ*I & φ*IV in brackish lakes and fresh lakes (b); Normalized φ*III&IV&V & φ*I&II in brackish lakes (c); Normalized φ*III&IV&V & φ*I&II in fresh lakes (d); a(350) & FI370 (e) ; and a(350) and HIX (f). The unit of normalized cumulative volume φ*i (i=I, II, III, IV, V) is QSU-nm2-[mg L-1 C], and a(350) is m-1.

46

Figure 8. RDA of CDOM spectroscopic parameters and the water quality parameters in (a) Brackish lakes and (b) Fresh lakes on the TQP. φ*I of brackish lakes was deleted due to large inflation factor (>20). The solid arrows and black font represent the environmental explanatory variables, and hollow allows and blue fonts were species variables, respectively. The unit of TN, TP, DOC and TSM was mg L-1; Chl-a was μg L-1; salinity is ‰; turbidity is NTU. Then the unit of a(254) and a(350) is m-1; SUVA254 is L mg C-1 m-1; φ*i (i=I, II, III, IV, V) is QSU-nm2-[mg L-1 C].

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Figure 9. (a) Box plots of normalized φ*III, φ*IV and φ*V in basins with large grass area (averaged area 14876 km2; N=10 basins, B1, B10, B19, B2, B11, B17, B12, B5, B20, B14), and basins with small grass area (averaged area 1976 km2; N=10 basins, B4, B6, B8, B9, B3, B15, B18, B16, B13, B17). (b) Box plots of normalized φ*III, φ*IV and φ*V in basins with large forest area (averaged area 633.9 km2; N=5 basins, B2, B1 B4, B11, B10), and in non-forest land. (c) Box plots of normalized φ*III, φ*IV and φ*V in basins with large unused land area (averaged area 9049 km2; N=10 basins, B1, B4, B2, B17, B3, B10, B11, B20, B19, B12), and basins with small grass area (averaged area 170 km2; N=10 basins, B6, B14, B8, B9, B15, B16, B7, B13, B5, B18). The black line and the hollow squares represent the median and mean values, respectively. The horizontal edges of the boxes denote the 25th and 75th percentiles; the whiskers denote the 10th and 90th percentiles. (d) The correlation between DOC and normalized humic like φ*V of 20 in basins across the TQP.

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Figure 10. The correlation between average DOC concentrations and a(254) in annual total sunshine hours > 2900h (a), annual total sunshine hours > 2800h (b) and annual total sunshine hours > 2600h (c). Then the correlation between average DOC concentrations and FI370 in annual total sunshine hours > 2900h (d), annual total sunshine hours > 2800h (e) and annual total sunshine hours > 2600h (f). Then the unit of a(254) is m-1.

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Highlights 

FDOM were explored from 244 samples of lakes in TQP

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FRI was conduct to characterize FDOM of fresh and brackish lakes

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Integrating effect of evapoconcentration, ultraviolet irradiance and landscapes

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Unique environmental factors control the FDOM properties in TQP

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