Substantial increase of organic carbon storage in Chinese lakes

Domain: earth_science
Published: 2024-09-14
Editorial outcome: minor_revision

Abstract
Previous studies typically assumed a constant total organic carbon (OC) storage in the lake water column, neglecting its significant variability within a changing world. Based on extensive field data and satellite monitoring techniques, we demonstrate considerable spatiotemporal variability in OC concentration and storage for 24,366 Chinese lakes during 1984–2023. Here we show that dissolved OC concentration is high in northwest saline lakes and particulate OC concentration is high in southeast eutrophic lakes. Along with increasing OC concentration and water volume, dissolved and particulate OC storage increase by 44.6% and 33.5%, respectively. Intensified human activities, water input, and wind disturbance are the key drivers for increasing OC storage. Moreover, higher OC storage further leads to an 11.0% increase in nationwide OC burial and a decrease in carbon emissions from 71.1% of northwest lakes. Similar changes are occurring globally, which suggests that lakes are playing an increasingly important role in carbon sequestration.

Introduction
Lakes serve as recipients, regulators, reactors, and storages in the global carbon cycle1,2. Globally, inland waters receive 2.9–5.1 Tg C yr−1 from terrestrial ecosystems1,3; lakes and reservoirs bury 0.06–0.25 Mt C yr−1 into the sediment4; and lakes and reservoirs emit 0.06–0.84 Tg C yr−1 of CO2 to the atmosphere5, and export 0.8–1.1 Tg C yr−1 to the oceans2. The remaining carbon is partly preserved in lake waters by either organic or inorganic forms6,7. Total organic carbon (OC) storage is defined as the product of lake OC concentration and water volume8. Lake OC storage affects several processes, including phytoplankton photosynthesis by absorbing sunlight6,9, dissolved oxygen consumption10,11, and lake carbon burial and outgassing4,5. Despite its importance, research on large-scale changes in lake OC storage is limited. Previous studies primarily focused on OC production and mineralization12,13, spatiotemporal changes in OC concentration and composition14,15, and/or carbon balance estimation by assuming a constant OC storage1–3. However, the amount of OC stored in lakes varies due to, for example, phytoplankton growth, water volume, and sewage discharge8,16,17.

In recent decades, lakes around the world have changed under the pressures of climate warming and intensifying human activities18. Algal blooms in many lakes are now occurring more frequently, with 8.8% of lakes worldwide experiencing an increase in bloom risk between the 1980s and 2010s19,20. Water volumes in 79.4% of global lakes has also increased since 2003, and one of the five hotspots of such change is China’s Tibetan Plateau21–23. Along with the changes in algal biomass, satellite-based monitoring of eutrophic Lake Taihu, China, has suggested that both dissolved (DOC) and particulate OC (POC) concentrations have fluctuated significantly in recent decades17,24. Lake OC storage is expected to vary greatly, as the changes in OC concentration and/or water volume. However, there is little research on the spatiotemporal variations of OC storage in various lakes. A study focusing on changes in OC storage across a wider range of lakes can provide valuable insights into the global carbon cycle.

China encompasses 24,366 lakes that are larger than 0.01 km2, and distributed across five limnetic zones characterized by diverse topographies and climatic conditions25,26: the Inner Mongolia-Xinjiang Lake zone (IMXL), the Tibetan Plateau Lake zone (TPL), the Yunnan-Guizhou Plateau Lake zone (YGPL), the Northeast Plain and Mountain Lake zone (NPML), and the Eastern Plain Lake zone (EPL). Across China, climatic conditions and human activities are clearly different on both sides of the Hu-line (Supplementary Fig. 3). Compared to the northwest IXML and TPL zones of the Hu-line, the three southeast lake zones had higher precipitation and more human activities25. Moreover, in recent decades, climatic warming has led to the expansion of lakes in the TPL23, and intensifying human activities have caused universal lake eutrophication in the EPL25. Therefore, China is an ideal region to explore the spatiotemporal changes in lake OC storage in a changing world.

In this work, based on extensive field data and satellite monitoring techniques, we derive DOC and POC concentrations from Landsat satellite data, calculate DOC and POC storage by multiplying water volumes, and reveal significant changes in OC concentrations and substantial increases in OC storage across China’s 24,366 lakes during 1984–2023 (see “Methods”).

Results
Spatiotemporal differences in OC concentration
Across the studied lakes with an area > 0.01 km2 from the HydroLAKES dataset i.e., that could be observed using Landsat satellite data (N = 24,366), DOC concentrations followed a spatial pattern of high in the northwest and low in the southeast and showed general increasing trends in different lakes during 1984–2023. Across China, mean DOC in the northwest IMXL and TPL zones (25.71 ± 24.72 mg L−1) was 2.5 times that in the southeast YGPL and EPL zones (10.29 ± 7.27 mg L−1) (Fig. 1a). DOC accumulated along with salinity in the northwest arid/semi-arid lakes. Conversely, strong precipitation diluted DOC concentrations in lakes of southeast China. For the in-situ data, there was a significant positive relationship between the logarithmic DOC concentration and conductivity, with Pearson’s r = 0.56 (N = 1636, p < 0.01, Supplementary Fig. 6a). Moreover, algal proliferation also contributed to high DOC concentrations in freshwater lakes (< 2000 μs cm−1), as suggested by a positive correlation between the logarithmic in-situ DOC and chlorophyll-a (Chl-a) concentrations (r = 0.23, N = 3624, p < 0.0001). Along with intensifying human activities (Supplementary Fig. 7h), 11,055 of the 15,303 freshwater lakes (i.e., 72.2%) in the southeast NPML and EPL zones showed increased DOC concentrations during 1984–2023 (Fig. 1b). However, 48.7% of lakes in the northwest TPL zone showed decreased DOC concentrations, which may be due to increasing precipitation (Supplementary Fig. 7f). Overall, mean DOC concentration in China’s lakes increased from 14.79 ± 12.49 mg L−1 in 1984 to 18.14 ± 21.75 mg L−1 in 2023, with an increasing trend in 65.4% of lake area.

In contrast to DOC, POC concentrations had a spatial pattern of low in the northwest and high in the southeast and demonstrated clear increasing trends in different lakes during 1984–2023 across China. Mean POC concentration was only 0.83 ± 0.19 mg L−1 in the northwest TPL zone, but as high as 4.85 ± 0.65 mg L−1 and 2.87 ± 1.08 mg L−1 in the southeast NPML and EPL zones, respectively (Fig. 1c). For the IMXL and YGPL zones across the Hu-line, lake POC concentration was also high in the southeast regions. High POC concentration was mainly due to algal proliferation. Total suspended matter (TSM) including algae accounted for only 14.1% of the spatial variations in POC concentration, indicated by the linear relationship between the logarithmic in-situ TSM and POC concentrations (N = 2026, R2 = 0.14, p < 0.0001). However, Chl-a alone could explain 71.0% (N = 2088, Supplementary Fig. 6b). Therefore, as population density generally grew in China (Supplementary Fig. 7h), 16,007 lakes whose area in total accounted for 80.6% showed increasing POC concentrations during 1984–2023 (Fig. 1d). For the northwest IMXL and TPL zones with low POC concentrations, 93.2% of lake area showed increasing trends. However, for the southeast NPML and EPL zones with high POC concentrations, 59.3% of lake area followed a downward trend, which we anticipate is due to water pollution control since 200027. Namely, human activities have been reducing the spatial differences in POC concentration.

Substantial increase in OC storage
Lake DOC was mainly stored in the TPL zone, and the increase in lake DOC storage during 1984–2023 also mainly occurred in the TPL zone. Combining the derived DOC concentration and water volume data from the HydroLAKES dataset in 2015 (Supplementary Table 1), we estimated that Chinese lakes collectively contained 39.43 Tg C of DOC. 83.2% of this total was stored in the TPL zone, the western endorheic regions of which were particularly notable for their high DOC concentrations (Figs. 1a, 2a). Lakes with low DOC concentrations in the EPL zone stored only 6.5% of DOC but 24.6% of water. Overall, lake-based DOC stock in China was dependent on water storage. The logarithmic DOC stock was linearly correlated with the logarithmic water volume, with R2 = 0.81 and p < 0.01 (Supplementary Fig. 6c). Due to increasing DOC concentrations (Fig. 1b) and water volume23, there was a substantial increase in lake DOC storage during 1984–2023. Of the 1125 lakes with time-series data of water volume, 63.7% experienced an increase in DOC storage (Fig. 2b, Supplementary Fig. 8); moreover, 92.0% of this total increase occurred in the TPL zone with a considerable increase in water volume23. According to the average growth rate of 96,295 t C yr−1 for the 1125 lakes, we estimated that ~13.05 Tg C of DOC was newly reserved in the 24,366 Chinese lakes during 1984–2023, an increase of 44.6%. In turn, we suggest that Chinese lakes acted as carbon sinks through storing more DOC.

In contrast to DOC storage, lake POC was concentrated in large quantities in lakes of the southeast with high POC concentrations. In 2015, the total POC storage was 2.14 Tg C, with 55.2% in the southeast EPL and NPML zones which stored only 29.3% of water (Fig. 2c). However, whilst lakes in the TPL zone stored 55.3% of water, their POC reserve was only 25.6%. Lake-based POC storage was generally determined by water volume, as suggested by the positive correlation between the logarithmic POC storage and water volume (R2 = 0.87, Supplementary Fig. 6d). Therefore, similar to DOC, POC storage in Chinese lakes was also elevated due to increasing water volume during 1984–2023. Of the 1125 lakes with time-series of water volume, 71.7% experienced increasing trends of POC storage (Supplementary Fig. 9); as many as 94.3% and 88.9% of lakes in the TPL and YGPL zones followed an upward trend during 1984–2023. Moreover, although 28.1% of lakes had decreasing POC storage, the decline trends were comparatively weak (Fig. 2d). Based on the average growth rate of 4033 t C yr−1 for the 1125 lakes, we estimated a total increase of 33.5% in POC storage for all 24,366 Chinese lakes during 1984–2023. Therefore, Chinese lakes also acted as carbon sinks by reserving more POC.

Drivers of change in Lake OC
Lake OC concentration is influenced by both anthropogenic and natural factors1,3. By applying the Random Forest analyses, we quantified the relative contributions of different factors to the spatiotemporal variations in OC concentration (Supplementary Note 4). Our results suggested that human activities contributed 50.2% to the spatial changes in DOC concentration and climate factors contributed 75.9% to the spatial changes in POC concentration. Furthermore, human activities, water input, and wind speed commonly had the largest contributions and were identified as the main drivers of the annual changes in OC concentration during 1984–2023 (Fig. 3).

Intensifying human activities elevated lake POC concentrations. Across China, 70.7% of lakes had an increasing population density within their basins during 1984–2023 (Supplementary Fig. 7h); population density was identified as the main factor for POC changes in 72.3% of lakes and had an average contribution of 33.1% (Fig. 3b). Human-induced eutrophication would increase lake phytoplankton, which itself is an important source of autochthonous POC (Supplementary Fig. 6b). For eutrophic lakes in the EPL zone, algal proliferation determines the quantity and composition of POC, and linear relationships between POC and Chl-a are commonly reported16,28. Moreover, Hou et al.19 reported that 88.9% of freshwater lakes in China showed increasing algal bloom frequencies during the 1980s–2010s. Meanwhile, half of the saline lakes in the remote TPL zone also experienced Chl-a increases due to increased riverine nutrient input29.

We suggest that increasing water input diluted lake DOC concentrations in the northwest IMXL and TPL zones. For the 7716 lakes situated in these two zones, increased precipitation and/or decreased evaporation occurred in 95.4% of them (Supplementary Fig. 7); and water input-related factors (precipitation, runoff, and evaporation) had a mean contribution of 37.3% for DOC concentration changes during 1984–2023 (Fig. 3a). Due to increasing water input associated with glacial ablation and/or climatic humidification, area expansion and salinity reduction are widespread for lakes in the IMXL and TPL zones23,30. Saline lakes are usually tail-end waters and terrigenous refractory DOC accumulates along with water evaporation12,31. In contrast, glacial meltwater and rainwater commonly have low DOC concentrations (around 1.0 mg L−1 or lower)32,33 and could dilute lake DOC in saline lakes. For lakes, Bositeng and Qinghai in the IMXL and TPL zones, decreased OC concentrations along with lake expansion have been reported34,35.

Strengthened wind mixing increased lake DOC concentrations in the southeast NPML, EPL, and YGPL zones during 1984–2023. For these three zones, wind speed increased for 58.0% of lakes (Supplementary Fig. 7g) and was the main factor influencing DOC changes in 70.6% of lakes (Fig. 3a). Wind disturbance likely increased lake DOC concentrations by promoting sediment DOC release, which was attributed to two favorable conditions. First, lakes in these zones are usually shallow with a mean water depth of 4.4 m26 and are commonly characterized by strong sediment resuspension36. Second, high algal proliferation in these zones elevates the OC content of the sediment19,37, which further leads to high DOC release from the pore water of sediment or from the photoproduction of OC in suspended sediment. For example, the suspended sediment of the eutrophic Lake Taihu had a DOC photoproduction of 0.29–0.83 mg L−1 38.

For a specific lake, the changes of OC concentration had important effects on OC storage, especially for DOC storage. For the 1125 lakes with time-series data of water volume (Supplementary Figs. 8, 9), changes in DOC concentration accounted for an average of 68.2% of the annual variations in DOC storage. In comparison, changes in POC concentration explained an average of 38.4% of the annual variations in POC storage. Note that the effect of OC concentration on OC storage greatly differed across lakes.

Implications for carbon deposition and emission
As OC storage increased from 1984 to 2023, lakes played a positive role in contributing to China’s goal of achieving carbon neutrality by 2060. Moreover, the amount of OC stored in lakes is expected to increase further. For the northwest TPL zone, lake water volume is expected to increase over the next 40 years39. For the southeast lakes, algal blooms are likely to increase due to climate warming20. In addition, the changes in OC concentration also have an essential influence on POC deposition to sediment and CO2 emission to the atmosphere.

The burial of OC in lakes is an important method of carbon sequestration. Globally, lakes and reservoirs have a total OC burial rate of 150 Tg C yr−1, which is positively correlated with water column-stored OC4. Across China, the OC accumulation rates (OCAR) of lakes with higher POC concentrations in the southeast were generally higher than those of lakes with lower POC concentrations in the northwest, with mean values of 22 ± 19.87 g C m−2 yr−1 and 7.78 ± 7.77 g C m−2 yr−1, respectively (Figs. 1c, 4a). For different lakes, OCAR after the 1950s was exponentially related to the climatological mean POC concentration during 1984–2023, with R2 = 0.35 and p < 0.01 (Fig. 4b). Based on the fitting formula and satellite-derived annual mean POC concentration, we estimated that total OC burial in Chinese lakes increased from 1.01 Tg C yr−1 in the 1980s to 1.13 Tg C yr−1 in the 2020s, with an increase of 11.0%. Therefore, increased lake POC concentration facilitated OC deposition to sediment during 1984–2023.

OC mineralization through microbial decomposition and photochemical oxidation acts as an essential CO2 source12,40. Globally, lakes with high OC concentrations tend to also have high CO2 emissions5,41. However, high POC concentrations also indicate high levels of photosynthesis, which consumes lake CO2 1. This study showed that in-situ CO2 concentration was positively correlated with the ratio of DOC to POC (Fig. 4c, d). Consequently, we found that changes in both DOC and POC concentrations led to varying effects on lake CO2 emissions from 1984 to 2023. For the southeast YGPL and EPL zones, 67.2% of lakes might have elevated CO2 concentrations due to increased DOC (Fig. 1b). In contrast, for the northwest IMXL and TPL zones, 71.1% of lakes likely experienced a decrease in CO2 concentration. This decline was primarily due to the reduction in DOC concentration caused by lake expansion. Additionally, decreased water conductivity and increased nutrient input further enhanced CO2 absorption through algal proliferation in these northwest zones29.

Based on these findings, substantial changes may have also occurred in the global lake carbon cycle. Firstly, increases in lake OC storage are likely prevalent worldwide. Satellite monitoring globally indicated that 80% of lakes experienced surface area expansion during 2003–201821. Moreover, increasing algal bloom frequency was detected in 62% of freshwater lakes during the 1980s–2010s19. Secondly, due to algal proliferation, elevated POC concentrations should increase POC deposition in sediments. For example, eutrophic lowland lakes in Europe exhibited OCAR values in excess of 50 g C m−2 yr−1 over the past century42. Thirdly, lake CO2 emissions are expected to vary with water eutrophication and/or expansion. Eutrophication has been observed to decrease lake CO2 emissions in the Midwestern U.S.43 while expanded lakes in boreal western Canada have shifted from CO2 sources to sinks44. These changes introduce significant uncertainties in estimating CO2 emissions from global lakes using different datasets5. Therefore, urgent efforts are needed to monitor carbon storage in the water column, CO2 emission to the atmosphere, and carbon burial in the sediment using global revisit satellite data.

Methods
Data acquisition
This study used in-situ data sampled during 2004–2023 (Dataset I in Supplementary Table 1, Supplementary Fig. 3). The in-situ data contain DOC, dissolved inorganic carbon (DIC), POC, Chl-a, TSM, pH, temperature, and conductivity values for 4,201 stations of 348 lakes. During field sampling, pH, temperature, and conductivity were recorded using a YSI Multi-parameter Sonde; lake water was filtrated through Whatman GF/F filters (0.7 μm pore size) to obtain DOC, DIC, POC, Chl-a, and TSM samples45,46. Then, in the lab, DOC, DIC, and POC concentrations were determined through the high-temperature combustion method46; TSM concentration was determined via gravimetry after drying; and Chl-a concentration was measured by the spectrophotometer technique45.

This study also obtained publicly available data (Datasets II-X in Supplementary Table 1). Among them, daily surface reflectance of Landsat-5/7/8/9 satellites during 1984–2023 was accessed via the Google Earth Engine (GEE) platform; daily water storage during 1984–2023 was sourced from the GloLakes dataset47; lake polygon/area/depth and river network/basin in 2015 were obtained from the HydroSHEDS dataset26; moreover, from the European Centre for Medium-Range Weather Forecasts (ECMWF), we obtained monthly averaged basin property data during 1984–2023, including population density, digital elevation model (DEM), evaporation, leaf area index of high vegetation (LAI_HighVeg), leaf area index of low vegetation (LAI_LowVeg), runoff, air temperature at 2 m height (Temp2m), precipitation, and wind speed. In addition, we also acquired OCAR data after the 1950s for 115 lakes in China through meta-analysis.

Remote retrieval of DOC concentration and storage
Lake DOC concentration is related to basin properties48, shows spatial variations within a single lake17, and tends to be higher in saline lakes compared to freshwater ones49. Based on these insights, this study developed an algorithm to estimate DOC concentration from Landsat reflectance data. First, a multilayer backpropagation neural network (MBPNN) with three hidden layers was trained to simulate lake-based annual mean DOC concentration using nine basin properties (Supplementary Fig. 4a). Second, using the simulated DOC, the matched Landsat reflectance at four visible and near-infrared bands (Supplementary Note 1), longitude, and latitude, two Random Forest models were built to retrieve DOC concentrations in freshwater and saline lakes, respectively, which were identified through a weighted decision tree method (Supplementary Note 2). The hyperparameters of the two Random Forests (Supplementary Note 2) were determined through the grid-search and K-fold cross-validation methods.

The parameterized models showed mean absolute percent difference (MAPD) values of 17.5% and 15.15% for the 30% testing data from freshwater and saline lakes, respectively (Supplementary Fig. 4b, c). The parameterized models were then applied to derive daily DOC concentrations from Landsat data, which had been processed to remove clouds and other non-water pixels using the pixel quality attributes provided by GEE. The developed models satisfactorily captured DOC concentrations from time-series Landsat data, with high values for saline/eutrophic lakes and low values for estuarine waters (Supplementary Fig. 10). Furthermore, annual mean DOC concentrations for 24,366 lakes during 1984–2023 were calculated using the satellite-derived daily DOC concentrations. Finally, DOC storage for 24,366 lakes in 2015 was calculated by multiplying annual mean DOC concentrations by water stocks from the HydroLAKES dataset26; annual DOC storage for 1125 lakes during 1984–2023 was calculated by multiplying annual mean DOC concentrations by water stocks from the GloLakes dataset47.

Remote retrieval of POC concentration and storage
Several algorithms were proposed to remotely derive POC concentrations in inland/coastal waters, and Chinese lakes in the southwest Tibet Plateau usually have low POC concentrations50,51. Based on the existing progress, this study developed a two-step algorithm to retrieve POC concentration from Landsat reflectance data (Supplementary Fig. 5a). First, four empirical equations (Eq. (1)) were constructed to roughly estimate POC concentration using the DEM, longitude, red band reflectance (Rred), and normalized difference carbon index51.

ln(POC) = −0.0005 × [DEM] + 1.13, R2 = 0.39
ln(POC) = 0.05 × [Longitude] − 4.18, R2 = 0.42
ln(POC) = 0.51 × ln(Rred) + 3.06, R2 = 0.31 (1)
ln(POC) = 1.72 × [NDCI] + 0.68, R2 = 0.32
[NDCI] = (Rred − Rblue) / (Rred + Rblue)

where Rblue denotes the blue band reflectance. Second, a Random Forest model was trained to precisely retrieve POC concentration using the roughly estimated results (Eq. (1)), longitude, latitude, DEM, and the matched Landsat reflectance (Supplementary Note 1, Supplementary Table 2).

The parameterized model obtained MAPD values of 16.7% and 16.5% for the training and testing data, respectively (Supplementary Fig. 5b, c). When they were applied to time-series Landsat data, the developed models obtained reasonable POC concentrations, with high values for eutrophic/turbid lakes and estuarine waters (Supplementary Fig. 10). Then, similar to the DOC retrieval, the parameterized models were applied to obtain daily POC concentrations and annual mean POC concentrations for 24,366 lakes during 1984–2023, calculate POC storage for 24,366 lakes in 2015, and calculate annual POC storage for 1125 lakes during 1984–2023.

Lake CO2 concentration calculation
Using the in-situ pH, temperature, and DIC data (Dataset I in Supplementary Table 1), this study calculated CO2 concentration based on the carbonate equilibrium method52. The method is commonly adopted when there is no in-situ CO2 53. The method is shown as Eq. (2):

[CO2] = [DIC] / ( (K1 / [H+]) + (K1 × K2 / [H+]2) + 1) (2)
[H+] = 10−pH

where [CO2] is the CO2 concentration (μmol L−1); [H+] indicates the H+ concentration (mol L−1); [DIC] denotes the DIC concentration (μmol L−1); K1 and K2 are the ionization fractions, which are calculated using water temperature (Tw) via Eq. (3):

log10 K1 = a1 + b1Tw + c1/Tw + d1 log10 Tw + e1/(Tw)2
log10 K2 = a2 + b2Tw + c2/Tw + d2 log10 Tw + e2/(Tw)2 (3)

where a1, a2, b1, b2, c1, c2, d1, d2, e1, and e2 are constants52.

Statistical analyses
This study matched Landsat reflectance for the measured DOC and POC concentrations (Supplementary Note 1), developed a weighted decision tree method to identify freshwater and saline lake types (Supplementary Note 2), delineated lake basin boundaries for 24,366 lakes (Supplementary Note 3), and calculated relative contributions of different factors to variations in DOC and POC concentration through the nonlinear Random Forest analyses (Supplementary Note 4). Moreover, we did regression analyses to determine the relationships between two variables and the changing trend of one variable along with time, with a significance level p < 0.05 (two-tailed test) being significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript, titled “Substantial increase of organic carbon storage in Chinese lakes”,\ninvestigated spatiotemporal changes in OC concentrations and OC storge in Chinese lakes using\nextensive field data and satellite monitoring techniques. This is an important paper that provides a\nnew perspective for estimating organic carbon storage in lakes. It is an interesting topic to the\nresearchers in the related areas but the paper needs improvement before acceptance for\npublication. My detailed suggestions and questions are as follows:\n1. Main Text, line 31, “lakes receive 2.9–5.1 Tg C/yr from terrestrial ecosystems” change “lakes” to\n“inland waters”;\nline31-32, “bury 0.06–0.25 Mt C/yr into the sediment”, change “lakes” to “lakes and reservoirs” or\n“inland waters”;\nline32-33,“emit 0.06 – 0.84 Tg C/yr of CO2 to the atmosphere”，\nlakes and reservoirs emit 0.06 – 0.84 Tg C/yr of CO2 to the atmosphere\n2. Main Text, line 47-48, the sentence “The total water volume of many lakes (79.4%) has also\nincreased since 2003” expressed not clearly, what does 79.4% refer to ?\n3. Main Text, line 82-83, “suggested by a positive correlation between the logarithmic in-situ DOC\nand chlorophyll-a (Chl-a) concentration (r = 0.23, N = 3,624)”, what is statistical p value ?\n4. Main Text, line 103-104, “concentration, indicated by the linear relationship between the\nlogarithmic in-situ TSM and POC concentration (N = 2,026, R2 = 0.14)”, what is statistical p value ?\n5. Main Text, references, line 359, change “Mendonca” to “Mendonça”.", "reviewer_2": "Lakes play an essential role in regulating the global carbon cycle and climate change. Although\nmany studies have provided insights into the carbon cycle of lakes, the spatiotemporal variations of\nlake OC storage on a large regional scale have not been reported. Previous studies have commonly\nassumed a fixed value for OC storage in lakes. As a matter of fact, OC storage in lakes showed\nobvious spatiotemporal variations along with water eutrophication, water level rise, and human\nactivities. It is of great significance to obtain OC storage spatiotemporally in lakes. On the one\nhand, OC storage reflects how much carbon have been stored in lakes. On the other hand, changes\nin OC storage also have important influences on lake CO2 emissions and carbon burial in\nsediments, two essential aspects of the global carbon cycle. Therefore, the dynamic acquisition of\nlake OC storage, as studied in this paper, plays an irreplaceable role in accurately quantifying the\nrole of lakes in the global carbon cycle.\nBased on measurements at 4,201 stations from 348 lakes across China, this study quantified the\nspatiotemporal variability of DOC and POC storage for 24,366 Chinese lakes during 1984-2023\nusing advanced satellite monitoring techniques. The authors report that DOC and POC storage in\nChinese lakes increased by 44.6% and 33.5%, respectively, which hasn’t been noticed before and\nis of great importance for understanding the carbon cycle in lakes. Using remote sensing\ntechniques, the authors innovatively conducted a remote sensing study of OC storage in a large\nregion of Chinese lakes by integrating basin characteristics and satellite reflectance, and revealed\nthe spatiotemporal variation characteristics of OC storage in all lakes in China for the first time. I\nbelieve the study has an important promoting role and reference value for the dynamic monitoring\nof global lake carbon cycle by remote sensing and deep learning techniques, and can compensate\nfor the insufficient spatiotemporal coverage of global lake monitoring OC data.\nOverall, the extensive field data are very valuable, the methods used are innovative and reasonable,\nand the findings are very important. The satellite-derived datasets of DOC and POC concentrations\nare also valuable for understanding carbon emission and carbon burial in Chinese lakes. In\naddition, I think the manuscript is well organized and written. Some revisions and clarifications are\nrequired before publication in the NC journal. I have the following suggestions for the authors to\nimprove the manuscript.\n1. “OC storage is expected to vary … water volume changes”. According to the above definition, OC\nconcentration and water volume variations will change the OC storage in the lake. In addition, the\nOC concentration in the lake also has obvious temporal variation characteristics. The authors here\nsay that OC storage varies with water volume, which is incomplete.\n2. “Across the studied lakes …”. How many lakes did the authors study in total? Do these lakes\ninclude all lakes in China? If not, how did the authors identify the lakes they studied? Although the\nauthors may have included relevant information in the online methods, it is still necessary to\nexplain it here so that readers can understand the relevant content.\n3. “the southeast NPML and EPL zones” and “the northwest TPL zone”. In my opinion, the TPL zone\nis located in southwest China, while the NPML zone is located in northeast China. How do the\nauthors divide the different lake zones? Why do the authors divide them this way? What is the\nreference standard? Please add any relevant information.\n4. “…demonstrated clear increasing tends…”. I don't quite understand what the authors mean by\nthis. Do the authors mean increasing trends in different lakes? Please clarify.\n5. “80.6% of lake area showed increasing POC concentration”. How is the lake area ratio calculated\nhere? Did the authors calculate the changes in POC concentrations of all pixels in Chinese lakes?\nFrom the whole paper, I think the authors only counted the average POC concentrations of lakes in\nChina, but did not perform statistics at the remote sensing pixel level.\n6. “Lake DOC was primarily concentrated in the TPL zone… increased substantially”. The statement\nis not clear. Do the authors mean that lake DOC is mainly stored in the TPL zone? Is the increase in\nlake DOC mainly in the TPL zone? Or do Chinese lakes show a significant increase in DOC storage?\n7. Does the lake proportion in Figure 2 represent the number of lakes or the area of lakes? How are\n“changes of DOC storage” obtained? Does the “changes of DOC storage” mean the linear fitted\nslope between DOC storage and year during 1986-2023? Also, I think the figure captions need to be\nlabelled separately.\n8. “… were identified as the main drivers …”. How did the authors identify the main drivers of\nchanges in DOC and POC concentrations in different lakes? Are the primary drivers those that\ncontribute more than 50%? Or does it refer to the factor that has the largest contribution among the\nfactors studied? The authors need to explain both in the main text and in the supplementary\ninformation, as the reader will often not necessarily read the supplementary information.\n9. “glacial meltwater and rainwater … DOC concentrations … 1.0 mg/L”. The DOC concentration in\nrainwater also has obvious spatiotemporal variation characteristics. Does 1.0 mg/L refer to the\naverage DOC concentration in lakes in the IMXL and TPL zones? Is the DOC concentration in glacial\nmeltwater also 1.0 mg/L? The study area of Gao et al. (2020) is Central Asia. Is the DOC\nconcentration in the glacial meltwater of Central Asia the same as in the IMXL and TPL zones?\nThe author needs to provide more information or a clear introduction.\n10. “… through pore water or photoproduction”. Pore water is usually derived from sediments. Does\nphotoproduction also refer to the oxidative decomposition of OC in sediments to produce DOC?\nEutrophic lakes are usually very turbid and have very low turbidity, so light has little or no access to\nthe bottom of the turbid water. If so, how can light oxidize OC in sediments and break it down into\nDOC? Please explain further.\n11. “OCAR was exponentially related to lake-based mean POC concentration”. How was the POC\nconcentration from the satellite remote sensing inversion matched to the OCAR data? Both POC\nconcentration and OCAR vary with time, and different time-matching schemes may give different\nresults. In addition, did the authors get annual OCAR data, like POC concentration?\n12. “…Europe exhibited OCAR values…”. As the previous comment, the authors need to give a\nspecific time for the OCAR values.\n13. “… measure the lake carbon cycle… using satellite data”. The \"lake carbon cycle\" is a very vague\ndefinition that includes various aspects of carbon composition, decomposition, and\ntransformation. What exactly do the authors mean by \"lake carbon cycle\"? As far as I know,\nalthough satellite data can be used to estimate CO2 emissions and carbon storage studied in this\npaper, there is no report on the use of satellite data to estimate carbon decomposition and\ntransformation.\n14. “monthly averaged basin property … and wind speed”. Where do these data come from? What is\nthe temporal coverage of these data? This sentence is incomplete.\n15. “… applied to retrieve annual mean DOC concentrations …” and “… was applied to retrieve\nannual mean POC concentrations …”. The models constructed in this paper should first be applied\nto Landsat data to calculate the daily DOC and POC concentrations, and then calculate the\naverage values according to the daily remote sensing results. The authors need to clearly explain\nthe relevant processes. Also, how did the authors remove clouds and other non-water pixels?\nDid the authors use the methods constructed in this study, or did they follow the published methods?\n16. For a given factor, the nonlinear Random Forest analysis indicated its relative contribution using\nthe Gini importance. The authors must explain why the Gini Importance index was chosen.\nCompared to the Permutation Importance and Boruta indexes, can the Gini Importance index\nbetter reflect the importance of the results of the Random Forest analysis?"}, "author_response": "Response: We thank the reviewer very much for these positive comments. In this revision,\nwe have addressed all the comments raised by the reviewer. To facilitate your assessment\nof the changes, we have highlighted our changes in red throughout the revised manuscript.\nAccording to the reviewers' comments, we have made the following major modifications:\n(1) We have added statistical p values for the model between the logarithmic DOC and\nChl-a and the model between the logarithmic TSM and POC. Please refer to the following\nResponses #1.3 and #1.4.\n(2) We have corrected the inappropriate statements about the lake carbon cycle and the\nincorrect spelling of the author’s name in the references. Please refer to the following\nResponses #1.1 and #15.\n(3) We have rephrased the statement about the figure showing the percent of global\nlakes with increasing water volumes since 2003. Please refer to the following Response #1.2.\nWith your help, we believe the manuscript has greatly improved, especially for model\nassessment and language expression. We hope that these changes have addressed your\nmain concerns. Your comments are invaluable in helping us improve our work.\n2\n1. Main Text, line 31, “lakes receive 2.9–5.1 Tg C/yr from terrestrial ecosystems” change\n“lakes” to “inland waters”;\nline31-32, “bury 0.06–0.25 Mt C/yr into the sediment”, change “lakes” to “lakes and\nreservoirs” or “inland waters”;\nline32-33,“emit 0.06 – 0.84 Tg C/yr of CO2 to the atmosphere”，lakes and reservoirs emit\n0.06 – 0.84 Tg C/yr of CO2 to the atmosphere\nResponse #1.1: We apologize for the inappropriate statements in the writing of this\nmanuscript and we thank the reviewer for pointing them out. According to the reviewer’s\ncorrections, we have changed “lakes” to “inland waters” for the statement about carbon\nfrom terrestrial ecosystems, changed “lakes” to “lakes and reservoirs” for the statement\nabout carbon burial into the sediment, and changed “lakes” to “lakes and reservoirs” for the\nstatement about carbon emission to the atmosphere. Please refer to lines 30-33 in the revised\nmanuscript:\nGlobally, inland waters receive 2.9 – 5.1 Tg C yr-1 from terrestrial ecosystems1,3; lakes and\nreservoirs bury 0.06 – 0.25 Mt C yr-1 into the sediment4; and lakes and reservoirs emit 0.06 – 0.84\nTg C yr-1 of CO\n2\nto the atmosphere5, and export 0.8 – 1.1 Tg C yr-1 to the oceans2.\n2. Main Text, line 47-48, the sentence “The total water volume of many lakes (79.4%) has also\nincreased since 2003” expressed not clearly, what does 79.4% refer to ?\nResponse #1.2: We thank the reviewer for pointing out this unclear expression. The figure\n“79.4%” refers to the percent of studied global lakes, which have experienced an increase in\n3\nwater volume since 2003. According to the reviewer’s comment, we have rephrased the\nsentence to make the expression clearer. Please refer to lines 47-49 in the revised manuscript:\nWater volumes in 79.4% of global lakes has also increased since 2003, and one of the five hotspots of\nsuch change is China’s Tibetan Plateau21-23.\n3. Main Text, line 82-83, “suggested by a positive correlation between the logarithmic in-situ\nDOC and chlorophyll-a (Chl-a) concentration (r = 0.23, N = 3,624)”, what is statistical p value ?\nResponse #1.3: We thank the reviewer for this comment. The statistical p value for the\npositive correlation between the logarithmic in-situ DOC and Chl-a concentration was 4.33\n× 10-45 (p < 0.0001). According to the reviewer’s comment, we have added the statistical p\nvalue to the statement. Please refer to lines 86-89 in the revised manuscript:\n… as suggested by a positive correlation between the logarithmic in-situ DOC and chlorophyll-a\n(Chl-a) concentrations (r = 0.23, N = 3,624, p < 0.0001).\n4. Main Text, line 103-104, “concentration, indicated by the linear relationship between the\nlogarithmic in-situ TSM and POC concentration (N = 2,026, R2 = 0.14)”, what is statistical p\nvalue ?\nResponse #1.4: Thank you to the reviewer for this comment. The statistical p value for the\npositive correlation between the logarithmic in-situ DOC and Chl-a concentration was 2.36\n× 10-70 (p < 0.0001). According to the reviewer’s comment, we had added the statistical p value\nto the statement. Please refer to lines 110-113 in the revised manuscript:\n… indicated by the linear relationship between the logarithmic in-situ TSM and POC concentrations\n4\n(N = 2,026, R2 = 0.14, p < 0.0001).\n5. Main Text, references, line 359, change “Mendonca” to “Mendonça”.\nResponse #1.5: We apologize for the error in writing this reference and thank the reviewer\nfor pointing it out. According to the reviewer’s correction, we have changed “Mendonca”\nto “Mendonça” in this revision. Please refer to lines 402-403 in the revised manuscript:\n4. Mendonça, R. et al. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694\n(2017).\nLakes play an essential role in regulating the global carbon cycle and climate change.\nAlthough many studies have provided insights into the carbon cycle of lakes, the\nspatiotemporal variations of lake OC storage on a large regional scale have not been\nreported. Previous studies have commonly assumed a fixed value for OC storage in lakes.\nAs a matter of fact, OC storage in lakes showed obvious spatiotemporal variations along\nwith water eutrophication, water level rise, and human activities. It is of great significance\nto obtain OC storage spatiotemporally in lakes. On the one hand, OC storage reflects how\nmuch carbon have been stored in lakes. On the other hand, changes in OC storage also have\nimportant influences on lake CO2 emissions and carbon burial in sediments, two essential\naspects of the global carbon cycle. Therefore, the dynamic acquisition of lake OC storage, as\nstudied in this paper, plays an irreplaceable role in accurately quantifying the role of lakes\nin the global carbon cycle.\nBased on measurements at 4,201 stations from 348 lakes across China, this study quantified\nthe spatiotemporal variability of DOC and POC storage for 24,366 Chinese lakes during\n1984-2023 using advanced satellite monitoring techniques. The authors report that DOC and\nPOC storage in Chinese lakes increased by 44.6% and 33.5%, respectively, which hasn’t been\nnoticed before and is of great importance for understanding the carbon cycle in lakes. Using\nremote sensing techniques, the authors innovatively conducted a remote sensing study of\nOC storage in a large region of Chinese lakes by integrating basin characteristics and satellite\nreflectance, and revealed the spatiotemporal variation characteristics of OC storage in all\nlakes in China for the first time. I believe the study has an important promoting role and\n6\nreference value for the dynamic monitoring of global lake carbon cycle by remote sensing\nand deep learning techniques, and can compensate for the insufficient spatiotemporal\ncoverage of global lake monitoring OC data.\nOverall, the extensive field data are very valuable, the methods used are innovative and\nreasonable, and the findings are very important. The satellite-derived datasets of DOC and\nPOC concentrations are also valuable for understanding carbon emission and carbon burial\nin Chinese lakes. In addition, I think the manuscript is well organized and written. Some\nrevisions and clarifications are required before publication in the NC journal. I have the\nfollowing suggestions for the authors to improve the manuscript.\nResponse: We appreciate the reviewer for highly recognizing the research value of this\nstudy, confirming the innovation of OC storage remote sensing in a large region of Chinese\nlakes by integrating basin characteristics and satellite reflectance, and approving the\nreliability of the results. We also thank the reviewer for providing detailed comments and\nsuggestions, which are of great help to us to improve the manuscript. To facilitate your\nassessment of the changes, we have highlighted our changes in red throughout the revised\nmanuscript. According to the reviewers' comments and suggestions, we have made the\nfollowing major changes to the manuscript:\n(1) We have made some minor changes to Fig. S1, Fig. 2, and Fig. 3. Please refer to the\nfollowing Responses #2.3, #2.7, and #2.8, respectively.\n(2) We have added some statements, which are essential for understanding the\nmanuscript. The new additions include: further clarification on lake identification\n(Response #2.2); the dividing criteria for the five lake zones (Response #2.3); the meaning of\n7\nthe main drivers (Response #2.8); the time-matching schemes for OCAR and POC data\n(Responses #2.11 and #2.12); the temporal coverage of basin property data (Response #2.14);\nthe removal processes of clouds and other non-water pixels (Response #2.15); and the reason\nfor choosing the Gini importance index (Response #2.16).\n(3) We have modified some incorrected and/or unclear statements. Modifications have\nbeen made to the statements on: the impact factors on OC storage changes (Response #2.1);\nthe increasing trends of POC concentrations in different lakes (Response #2.4); the\ncalculation method for the area ratio of lakes with increasing POC concentrations (Response\n#2.5); DOC storage in the TPL zone (Response #2.6); the assumption of DOC concentration\nin glacial meltwater and rainwater (Response #2.9); photoproduction of OC in suspended\nsediment (Response #2.10); and the vague phrase of “lake carbon cycle” (Response #2.13).\nWe would like to thank the reviewer for his/her valuable comments and suggestions, which\nare very beneficial for the improvement of our manuscript. We sincerely hope that you will\nbe satisfied with the revised manuscript.\n1. “OC storage is expected to vary … water volume changes”. According to the above\ndefinition, OC concentration and water volume variations will change the OC storage in the\nlake. In addition, the OC concentration in the lake also has obvious temporal variation\ncharacteristics. The authors here say that OC storage varies with water volume, which is\nincomplete.\nResponse #2.1: Yes, the reviewer is correct. Apart from water volumes, OC concentrations\nin lakes also have temporal variation characteristics. Therefore, the statement that OC\n8\nstorage varies with water volume is incomplete. In this revision, we have changed the\nstatement to emphasize that lake OC storage also varies with changes in OC concentration.\nPlease refer to lines 52-53 in the revised manuscript:\nLake OC storage is expected to vary greatly, as the changes in OC concentration and/or water\nvolume.\n2. “Across the studied lakes …”. How many lakes did the authors study in total? Do these\nlakes include all lakes in China? If not, how did the authors identify the lakes they studied?\nAlthough the authors may have included relevant information in the online methods, it is\nstill necessary to explain it here so that readers can understand the relevant content.\nResponse #2.2: We thank the reviewer for pointing out this unclear statement. We agree\nwith the reviewer that it is necessary to explain how many lakes were studied here so that\nreaders can understand the relevant content. In this study, we studied 24,366 lakes in total.\nBased on the HydroLAKES dataset (Supplementary Table S1), we identified the studied\nlakes using an area threshold of > 0.01 km2. The studied lakes are those that could be\nobserved using Landsat satellite data with a spatial resolution of 30 m. In this revision, we\nhave added this information. Please refer to lines 76-79 in the revised manuscript:\nAcross the studied lakes with an area > 0.01 km2 from the HydroLAKES dataset i.e., that could\nbe observed using Landsat satellite data (N = 24,366) ....\n3. “the southeast NPML and EPL zones” and “the northwest TPL zone”. In my opinion, the TPL zone\nis located in southwest China, while the NPML zone is located in northeast China.\nway? What is the reference standard? Please add any relevant information.\nResponse #2.3: We thank the reviewer for bringing this to our attention. We forgot to\nillustrate the dividing criteria of the five lake zones across China in the original manuscript.\nBecause climatic conditions and human activities in China are clearly different on both sides\nof the Hu-line, we further divided the five lake zones into the northwest and southeast parts\naccording to the Hu-line (Supplementary Fig. S3). The northwest part includes the IMXL\nand TPL zones, and southeast part includes the YGPL, NPML, and EPL zones. In this\nrevision, we had added the information about the dividing criteria and delineated the Hu-\nline in Supplementary Fig. S3. Please refer to lines 62-65 in the revised manuscript:\nAcross China, climatic conditions and human activities are clearly different on both sides of the Hu-\nline (Supplementary Fig. S3). Compared to the northwest IXML and TPL zones of the Hu-line, the\nthree southeast lake zones had higher precipitation and more human activities25.\nSupplementary Table S1). The inserted global map was obtained from Google Earth.\n4. “…demonstrated clear increasing tends…”. I don't quite understand what the authors\nmean by this. Do the authors mean increasing trends in different lakes? Please clarify.\nResponse #2.4: We apologize for this unclear statement. Yes, the sentence means that DOC\nconcentrations demonstrated clear increasing trends in different lakes across China during\n1984-2023. In this revision, we have clarified the statement. Moreover, we have also made\nsimilar changes to the statement on POC concentrations. Please refer to lines 76-79 and lines\n103-105 in the revised manuscript:\nLines 76-79: … and showed general increasing trends in different lakes during 1984-2023.\nLines 103-105: … and demonstrated clear increasing trends in different lakes during 1984-2023\nacross China.\n11\n5. “80.6% of lake area showed increasing POC concentration”. How is the lake area ratio\ncalculated here? Did the authors calculate the changes in POC concentrations of all pixels in\nChinese lakes? From the whole paper, I think the authors only counted the average POC\nconcentrations of lakes in China, but did not perform statistics at the remote sensing pixel\nlevel.\nResponse #2.5: We thank the reviewer for this thoughtful suggestion. Yes, we only counted\nthe average lake POC concentrations and did not perform statistics at the remote sensing\npixel level. That is, we did not calculate the changes in POC concentrations of all pixels. The\nlake area ratio here was calculated by dividing the total area of lakes with increasing POC\nconcentrations by the total area of all studied lakes. In this revision, we have rephrased the\nsentence to clarify it. Please refer to lines 114-116 in the revised manuscript:\nTherefore, as population density generally grew in China (Supplementary Fig. S7h), 16,007 lakes\nwhose area in total accounted for 80.6% showed increasing POC concentrations during 1984-\n2023 (Fig. 1d).\n6. “Lake DOC was primarily concentrated in the TPL zone… increased substantially”. The\nstatement is not clear. Do the authors mean that lake DOC is mainly stored in the TPL zone?\nIs the increase in lake DOC mainly in the TPL zone? Or do Chinese lakes show a significant\nincrease in DOC storage?\nResponse #2.6: We apologize for this unclear statement. Yes, we meant that lake DOC was\nmainly stored in the TPL zone and the increase in DOC storage mainly occurred in the TPL\n12\nzone. Chinese lakes collectively contained 39.43 Tg C of DOC in 2015, and 83.2% of this total\nDOC storage was stored in the TPL zone. Furthermore, of the 1,125 lakes with time-series\nof water volume, 63.7% experienced an increase in DOC storage, and 92.0% of the total\nincrease occurred in the TPL zone. In this revision, we have rephrased the sentence to clarify\nthem. Please refer to lines 123-124 in the revised manuscript:\nLake DOC was mainly stored in the TPL zone, and the increase in lake DOC storage during\n1984-2023 also mainly occurred in the TPL zone.\n7. Does the lake proportion in Figure 2 represent the number of lakes or the area of lakes?\nHow are “changes of DOC storage” obtained? Does the “changes of DOC storage” mean the\nlinear fitted slope between DOC storage and year during 1986-2023? Also, I think the figure\ncaptions need to be labelled separately.\nResponse #2.7: We appreciate this comment by the reviewer and have modified Figure 2\naccordingly. In Fig. 2, “the lake proportion” represents the number of lakes, and the\n“changes of DOC storage” represents the linearly fitted slope between DOC storage and\nyear during 1986-2023. In this revision, we have modified Fig. 2 and its caption to clarify\nthis information. Moreover, we have also separately labelled the figure caption. Please refer\nto Fig. 2 in the revised manuscript:\n2015; b lake-based changes for 1,125 lakes during 1984-2023; c 1.0° grid-based POC stock for\n24,366 lakes in 2015; and d lake-based changes for 1,125 lakes during 1984-2023. The changes of\nDOC storage mean the linearly fitted slope between DOC storage and year during 1984-2023.\nSource data are provided as a Supplementary Data file.\n8. “… were identified as the main drivers …”. How did the authors identify the main drivers\nof changes in DOC and POC concentrations in different lakes? Are the primary drivers those\nthat contribute more than 50%? Or does it refer to the factor that has the largest contribution\namong the factors studied? The authors need to explain both in the main text and in the\nsupplementary information, as the reader will often not necessarily read the supplementary\ninformation.\nnot explain the meaning of the main driver in the original manuscript. The primary driver\nrefers to the factor that had the largest contribution among the impact factors examined.\nAccording to the reviewer’s comments, we have added this information in lines 176-177, the\ncaption of Fig. 3, and the Supplementary Note S4:\nLines 176-177: The main driving factors indicate those which have the largest contributions.\nSupplementary Note S4: According to the relative contributions of different factors, we determined\nthe main driving factors which have the largest contributions for different lakes.\nFig. 3. Influences of different factors on DOC and POC variations. The main driving factors for\nthe annual changes of a DOC and b POC concentrations during 1984-2023. The main driving\nfactors indicate those which have the largest contributions. The figures show the main factors\nwith the largest mean contributions for lakes in the 1.0° grids. c The driving processes of the\nmain factors for the changes in DOC and POC. DOC Conc.: DOC concentration. Source data are\nprovided as a Supplementary Data file.\n9. “glacial meltwater and rainwater … DOC concentrations … 1.0 mg/L”. The DOC\nconcentration in rainwater also has obvious spatiotemporal variation characteristics. Does\n1.0 mg/L refer to the average DOC concentration in lakes in the IMXL and TPL zones? Is the\nCentral Asia. Is the DOC concentration in the glacial meltwater of Central Asia the same as\nin the IMXL and TPL zones? The author needs to provide more information or a clear\nintroduction.\nResponse #2.9: We thank the reviewer for pointing out this incorrect assumption. In the\noriginal manuscript, we assumed a DOC concentration of about 1.0 mg L-1 for glacial\nmeltwater and rainwater in the IMXL and TPL zones. However, DOC concentrations in both\nglacial meltwater and rainwater have obvious spatiotemporal variation characteristics.\nAccording to Gao et al. (2020), the DOC concentration of Muz Taw Glacier has a variation\nrange of 0.22 – 7.71 mg L-1, with an average value of 1.12 ± 1.66 mg L-1. In addition, DOC\nconcentration in the glacial meltwater of Central Asia is not the same as that in the IMXL\nand TPL zones. According to Safieddine and Heald (2017), the DOC concentration in\nrainwater varies from 0.01 mg L-1 to 10 mg L-1 globally. Therefore, the assumption of DOC\nconcentration (about 1.0 mg L-1) for glacial meltwater and rainwater in the IMXL and TPL\nzones was not correct. In this revision, we have removed the incorrect assumption and\nrephrased the related sentence. Please refer to lines 202-204 in the revised manuscript:\nIn contrast, glacial meltwater and rainwater commonly have low DOC concentrations (around\n1.0 mg L-1 or lower)32,33 and could dilute lake DOC in saline lakes.\n10. “… through pore water or photoproduction”. Pore water is usually derived from\nsediments. Does photoproduction also refer to the oxidative decomposition of OC in\nsediments to produce DOC? Eutrophic lakes are usually very turbid and have very low\n16\nturbidity, so light has little or no access to the bottom of the turbid water. If so, how can light\noxidize OC in sediments and break it down into DOC? Please explain further.\nResponse #2.10: We thank the reviewer for pointing out this misleading statement. As the\nreviewer said, eutrophic lakes in the southeast lake zones are usually turbid, so there is little\nor no light reaching the bottom of the turbid water. Therefore, light is less likely to oxidize\nthe OC in the sediment and break it down into DOC. However, eutrophic lakes are usually\ncharacterized by shallow depths and high sediment resuspension, which favor DOC release\nfrom the photoproduction of OC in suspended sediment. Furthermore, high OC content in\nthe sediment also favors DOC release from the pore water of the sediment. Based on these\nunderstandings, we have modified the misleading statement. Please refer to lines 213-215 in\nthe revised manuscript:\nSecond, high algal proliferation in these zones elevates the OC content of the sediment19,37, which\nfurther leads to high DOC release from the pore water of sediment or from the photoproduction of\nOC in suspended sediment.\n11. “OCAR was exponentially related to lake-based mean POC concentration”. How was\nthe POC concentration from the satellite remote sensing inversion matched to the OCAR\ndata? Both POC concentration and OCAR vary with time, and different time-matching\nschemes may give different results. In addition, did the authors get annual OCAR data, like\nPOC concentration?\nResponse #2.11: Yes, we agree with the reviewer. Both POC concentration and OCAR vary\nwith time, different time-matching schemes may give different results. At present, it is less\n17\nlikely to get annual OCAR data, as with POC concentration. In this study, we only obtained\nOCAR data after the 1950s for 115 Chinese through meta-analysis. To match the lake-based\nOCAR data in time, we used the satellite-derived climatological mean POC concentrations\nof the relative lakes with OCAR data. In this revision, we have given the time spans for both\nthe OCAR data and the POC concentrations. Please refer to lines 239-241 in the revised\nmanuscript:\nFor different lakes, OCAR after the 1950s was exponentially related to the climatological mean\nPOC concentration during 1984-2023, with R2 = 0.35 and p < 0.01 (Fig. 4b).\n12. “…Europe exhibited OCAR values…”. As the previous comment, the authors need to\ngive a specific time for the OCAR values.\nResponse #2.12: We thank the reviewer for this suggestion. The OCAR value was cited here\nwith reference to Anderson et al. (2014). The time span for the OCAR value was “over the\npast century”. In this revision, we have added this information. Please refer to lines 271-272\nin the revised manuscript:\nFor example, eutrophic lowland lakes in Europe exhibited OCAR values in excess of 50 g C m-2 yr-1\nover the past century42.\n13. “… measure the lake carbon cycle… using satellite data”. The \"lake carbon cycle\" is a\nvery vague definition that includes various aspects of carbon composition, decomposition,\nand transformation. What exactly do the authors mean by \"lake carbon cycle\"? As far as I\nknow, although satellite data can be used to estimate CO2 emissions and carbon storage\n18\nstudied in this paper, there is no report on the use of satellite data to estimate carbon\ndecomposition and transformation.\nResponse #2.13: Yes, we agree with the reviewer that the phrase “lake carbon cycle” has a\nvery vague definition here. Although satellite data can be used to estimate many parameters\nrelated to the lake carbon cycle, this study only reported information on carbon storage, CO\n2\nemissions, and carbon burial in the sediment. Moreover, at present, it is difficult to monitor\ncarbon decomposition and transformation using satellite data. Therefore, according to the\nresearch scope of this study, we have replaced the vague phrase of “lake carbon cycle” with\n“carbon storage in the water column, CO emission to the atmosphere, and carbon burial to\n2\nthe sediment”. Please refer to lines 277-279 in the revised manuscript:\nTherefore, urgent efforts are needed to monitor carbon storage in the water column, CO\n2\nemission to the atmosphere, and carbon burial in the sediment using global revisit satellite\ndata.\n14. “monthly averaged basin property … and wind speed”. Where do these data come from?\nWhat is the temporal coverage of these data? This sentence is incomplete.\nResponse #2.14: We apologize for this incomplete sentence. The monthly averaged basin\nproperty data were obtained from the European Centre for Medium-Range Weather\nForecasts. These data have a temporal coverage of 1984-2023. In this revision, we have\nsupplemented this information. Please refer to lines 297-302 in the revised manuscript:\n… moreover, from the European Centre for Medium-Range Weather Forecasts (ECMWF), we\nobtained monthly averaged basin property data during 1984-2023, including population density,\n19\ndigital elevation model (DEM), evaporation, leaf area index of high vegetation (LAI_HighVeg), leaf\narea index of low vegetation (LAI_LowVeg), runoff, air temperature at 2 m height (Temp2m),\nprecipitation, and wind speed.\n15. “… applied to retrieve annual mean DOC concentrations …” and “… was applied to\nretrieve annual mean POC concentrations …”. The models constructed in this paper should\nfirst be applied to Landsat data to calculate the daily DOC and POC concentrations, and\nthen calculate the average values according to the daily remote sensing results. The authors\nneed to clearly explain the relevant processes. Also, how did the authors remove clouds and\nother non-water pixels? Did the authors use the methods constructed in this study, or did\nthey follow the published methods?\nResponse #2.15: We appreciate the reviewer’s comments and have modified the manuscript\naccordingly. As the reviewer mentioned, the models constructed in this study were first\napplied to Landsat data to derive daily DOC and POC concentrations, and then we\ncalculated the annual mean values according to the daily remote sensing results. Moreover,\nthe clouds and other non-water pixels of the Landsat data were removed using the pixel\nquality attributes provided by GEE. In this revision, we have explained the relevant\nprocesses for the remote sensing inversion of DOC and POC concentrations. Please refer to\nlines 320-327 and lines 346-352 in the revised manuscript:\nLines 320-327: The parameterized models were then applied to derive daily DOC concentrations\nfrom Landsat data, which had been processed to remove clouds and other non-water pixels using the\npixel quality attributes provided by GEE. The developed models satisfactorily captured DOC\n20\nconcentrations from time-series Landsat data, with high values for saline/eutrophic lakes and low\nvalues for estuarine waters (Supplementary Fig. S10). Furthermore, annual mean DOC\nconcentrations for 24,366 lakes during 1984-2023 were calculated using the satellite-derived daily\nDOC concentrations.\nLines 346-352: When they were applied to time-series Landsat data, the developed models obtained\nreasonable POC concentrations, with high values for eutrophic/turbid lakes and estuarine waters\n(Supplementary Fig. S10). Then, similar to the DOC retrieval, the parameterized models were applied\nto obtain daily POC concentrations and annual mean POC concentrations for 24,366 lakes during\n1984-2023, calculate POC storage for 24,366 lakes in 2015, and calculate annual POC storage for\n1,125 lakes during 1984-2023.\n16. For a given factor, the nonlinear Random Forest analysis indicated its relative\ncontribution using the Gini importance. The authors must explain why the Gini Importance\nindex was chosen. Compared to the Permutation Importance and Boruta indexes, can the\nGini Importance index better reflect the importance of the results of the Random Forest\nanalysis?\nResponse #2.16: We thank the reviewer for this comment about the nonlinear Random\nForest analysis in Supplementary Note S4. In the Random Forest analysis, the Gini,\nPermutation, and Boruta importance indexes are usually used to evaluate the importance of\ndifferent features. Each of these three indexes have advantages and disadvantages.\nAccording to Rodríguez-Pérez and Bajorath (2021), the Gini importance index was chosen\nfor this study because it is computationally efficient, suitable for large-scale analysis, and\n21\neasy to understand and explain. In this revision, we have supplemented the following\nstatements to explain why we chose the Gini importance index in the Random Forest\nanalysis. Please refer to Supplementary Note S4:\nIn the Random Forest analysis, the Gini, Permutation, and Boruta importance indexes are usually\nused to evaluate the importance of different features. Each of these three indexes has its advantages\nand disadvantages. According to Rodríguez-Pérez and Bajorath (2021)10, we chose the Gini\nimportance index because it is computationally efficient, suitable for large-scale analysis, and easy to\nunderstand and explain. For a given factor, the non-linear Random Forest analysis indicated its\nrelative contribution using the Gini importance, which was equivalent to the mean decrease in Gini\nimpurity calculated as the normalized sum of the impurity decrease values for all nodes10. The\ncontribution of each factor had a value of 0 – 100%, and the sum of the contributions of the eight\nfactors was 100%.\n22"}, {"round": 2, "reviewer_comments": {"reviewer_2": "I have carefully read the revision of the paper. The manuscript has improved greatly after the first revision round. The authors corrected the paper according to my comments and cleared up all my questions. I think that the paper can be accepted for publication. I do not have further questions or comments."}, "author_response": "Response: We thank the reviewer for acknowledging our changes. The reviewer’s\ncomments and suggestions from the first round of review have help us to improve our\nmanuscript considerably."}]

Decision letter